One Giant Leap for Model-Based Engineering

 

Aviation Week & Space Technology

Analysis & Commentary — June 2026

Systems Engineering • Digital Engineering • Space

Airbus Researchers Use Apollo 11 as a Proving Ground for the Industry's New Systems Modeling Standard — and Find a Language Ready for Prime Time, but a Tool Ecosystem Still Catching Up

A new peer-reviewed paper from Airbus Central R&T reimagines the Apollo 11 mission in SysML v2, the aerospace industry's freshly ratified modeling language — simultaneously benchmarking the standard's capabilities and providing the MBSE community's most comprehensive open-source reference model to date.

Analysis | Defense & Space Technology Desk

Washington / Hamburg, June 27, 2026

Bottom Line Up Front The Object Management Group finalized SysML v2 in July 2025 after eight years of development, delivering a formal textual syntax, a standardized API, and dramatically enhanced semantic rigor over its predecessor. In a paper published this month in the INCOSE journal Systems Engineering, Airbus researchers Philipp Helle and Gerrit Schramm have produced a comprehensive, five-layer SysML v2 model of the Apollo 11 mission — the most ambitious open-source SysML v2 case study yet released — demonstrating that the language can handle genuine system-of-systems complexity. Their evaluation finds SysML v2's core capabilities compelling but flags an immature commercial tool ecosystem as the primary near-term adoption barrier. The U.S. Department of Defense, NASA, and the European Space Agency are all actively transitioning to SysML v2; the timing of the Airbus model gives the community a common benchmark it has lacked.

A New Standard Needs a Stress Test

When the Object Management Group approved the final adoption of Systems Modeling Language Version 2 on July 21, 2025, it marked the culmination of an effort that had begun in 2017, when the international standards body first solicited industry requirements for a next-generation modeling language. The result — three interlocking specifications comprising SysML v2.0, the foundational Kernel Modeling Language (KerML) 1.0, and a standardized Application Programming Interface — represented the most significant overhaul in the nineteen-year history of systems modeling standardization.

The INCOSE president Ralf Hartmann called it "an essential contribution to our strategic ambition to evolve systems engineering to a fully model-based discipline." Tool vendors including PTC, Qualtech Systems, and a wave of newer entrants were equally effusive. Yet in the weeks following the announcement, a familiar problem reasserted itself: the most technically sophisticated modeling language in the world is only as useful as the models built with it, and the community had almost no large-scale, publicly accessible SysML v2 models to work from.

It was precisely that gap that Philipp Helle and Gerrit Schramm of Airbus Central Research & Technology in Hamburg set out to close. Their approach — applying the new language to the most celebrated systems engineering achievement in history — is both strategically shrewd and genuinely illuminating.

Why Apollo 11?

The selection of the Apollo 11 mission as the modeling subject was deliberate on multiple counts. From a technical standpoint, the mission constitutes a genuine system-of-systems (SoS): a Saturn V launch vehicle, a Command and Service Module, a Lunar Module, crew, ground systems, and a mission timeline of extraordinary complexity, with tightly coupled temporal dependencies — staging sequences, orbital insertion windows, Trans-Lunar Injection burns — that stress any modeling language attempting to capture both structure and behavior in a unified representation.

From a communicative standpoint, the mission is universally legible. Every engineer understands the Saturn V's five F-1 engines, the separation of the Command Module Columbia from the Lunar Module Eagle, and the 102-hour, 45-minute flight to lunar orbit. That shared understanding makes the Apollo 11 model an exceptionally effective vehicle for demonstrating — and critiquing — the expressive power of SysML v2 to an audience that extends well beyond modeling specialists.

Finally, the mission is exceptionally well-documented. Decades of NASA historical records, technical specifications, flight plans, and the official history Chariots for Apollo (Brooks, Grimwood, and Swenson, 1979, updated 2012) provided a rich factual substrate against which model fidelity could be verified.

The CoSMA Framework: Five Questions, Five Layers

Helle and Schramm structured the Apollo 11 model using CoSMA (Complex System Modeling and Architecting), a proprietary Airbus framework derived from the company's earlier R-MOFLT methodology. The framework organizes any complex system model around five disciplined questions:

• Purpose Layer: "Why does this system exist?" — Stakeholder needs, mission goals, 60 verifiable mission requirements
• Operational Layer: "When do things happen?" — 13 chronological mission phases, from launch preparation to quarantine recovery
• Functional Layer: "What must the system do?" — A technology-agnostic hierarchy of functions with specified inputs and outputs
• Logical Layer: "What abstract parts are responsible?" — Components like LaunchSystem and Spacecraft defined by role, not physical packaging
• Technical Layer: "How is it physically built?" — Concrete hardware definitions: S-IC first stage (2,300,000 kg gross mass, five F-1 engines), Apollo Command Module (5,556 kg), and their port-defined physical interfaces

The Language Under Examination

The paper's second primary contribution is a rigorous empirical evaluation of what SysML v2 can and cannot yet do in practice. The findings are nuanced, and worth examining feature by feature.

Textual notation. The most transformative change from SysML v1 is the introduction of a formal, standardized textual syntax. Where SysML v1 was primarily a graphical notation — diagrams first, interoperability as an afterthought — SysML v2 treats text as the primary, machine-readable representation of the underlying model graph. This single change has profound downstream consequences. Models stored as text can be version-controlled with standard tools like Git, enabling collaborative engineering workflows familiar from software development. They can be parsed and queried by any standards-compliant tool. And, as the research community has rapidly recognized, the textual format dramatically lowers the barrier for AI-assisted model generation: multiple recent studies presented at the 2024 INCOSE International Symposium found that Large Language Models could generate syntactically valid SysML v2 far more readily than they could produce correct SysML v1 diagrams.

Specialization and inheritance. SysML v2 introduces a clean, consistent hierarchy of specialization mechanisms — subclassification, subsetting, and redefinition — that apply uniformly across all model element types. In SysML v1, inheritance behaved differently for structural versus behavioral elements, a source of chronic inconsistency across tool implementations. The Apollo 11 model exploits redefinition extensively: the abstract RocketStage definition is specialized into the concrete S-IC, which redefines gross mass (2,300,000 kg), dry mass (131,000 kg), and explicitly instantiates five F-1 engine parts. The result is a model hierarchy that is both human-readable and formally machine-verifiable.

Individual definitions. Perhaps the most conceptually significant new feature of SysML v2 is the individual keyword, which allows the model to distinguish between a design type (the general Saturn V rocket specification) and a specific, unique physical artifact (launch vehicle SA-506, the particular vehicle that flew Apollo 11). In SysML v1, this distinction — fundamental to digital twin applications — was notoriously difficult to implement consistently. The Airbus model uses individuals to represent specific crew members by name, specific flight hardware by serial number, and specific mission artifacts: SA-506 as the launch vehicle, CSM-107 Columbia as the Command Module (now on display at the National Air and Space Museum), and LM-5 Eagle as the Lunar Module, whose descent stage remains at Tranquility Base and whose ascent stage, per the model's documentation, may still be in lunar orbit.

Formal requirements with constraints. SysML v2 elevates requirements from text fields with identifiers to first-class model elements that own typed attributes and executable constraints. The soft-landing requirement in the Apollo 11 model formally declares maxVerticalVelocity = 2 [SI::'m/s'] and maxHorizontalVelocity = 0.5 [SI::'m/s'], with a require constraint block that can generate an automated pass/fail verdict when actual values are supplied. This represents a fundamental shift from the SysML v1 world, where verifying a requirement typically required exporting model data to external tools for evaluation.

Quantities and units. The native integration of an ISO/IEC 80000-1:2022-compliant quantities and units library eliminates a persistent ambiguity in SysML v1 models, where engineers frequently mixed unit systems without formal safeguards. The Apollo 11 model's Tsiolkovsky rocket equation — implemented as a native calc def calculateDeltaV — automatically enforces unit consistency: a mass in pounds cannot be added to a mass in kilograms without an explicit conversion.

Variability modeling. The new variation and variant keywords allow product-line configurations to be specified directly within the system architecture, without the proprietary profile extensions that made SysML v1 variability models notoriously non-portable. The paper illustrates this with the S-IVB third stage, which flew in two configurations: the 200-series (non-restartable J-2 engine, used on Saturn IB earth-orbital missions) and the 500-series (restartable J-2 with auxiliary propulsion system, required for Trans-Lunar Injection on Saturn V lunar missions). The model enforces, through automated validation, that a lunar mission cannot be inadvertently configured with the non-restartable variant.

"The SysML v2 textual notation serves as a bridge between the precision of code and the semantic richness of systems modeling."
— Helle and Schramm, Systems Engineering, June 2026

The Digital Thread, Made Concrete

One of the most practically significant aspects of the Airbus paper is its demonstration of end-to-end traceability — the "digital thread" that connects a stakeholder's concern to the physical component that ultimately addresses it. The model achieves this through two complementary relationship types: refines, used for top-down decomposition from abstract needs to concrete requirements, and satisfy, used for bottom-up attribution of requirements to solution elements.

The traceability chain for the lunar landing requirement is illustrative. The U.S. government's desire for international technological prestige (Stakeholder Need SHN-N004) refines into the LunarSurfaceLandingAndAscent capability, which refines into the high-level requirement HLR-R002 (soft lunar landing by July 20, 1969), which refines into the functional requirement FLR-R082 (system shall slow the vehicle to a safe landing velocity), which refines into the technical requirement CLR-R050 (vertical velocity ≤ 3.05 m/s, horizontal velocity ≤ 1.22 m/s at touchdown). CLR-R050 is then formally satisfied by the LunarModuleDescentStage part definition. A systems engineer can traverse this chain in either direction — from a Cold War political objective down to a specific thrust command, or from a component specification back up to the mission imperative it serves.

SysML v2's standardized API — the third specification in the July 2025 package — extends this traceability potential beyond the model itself. The API assigns persistent Universally Unique Identifiers to all model elements across versions, enabling external tools (simulation environments, stress-analysis packages, requirements management systems) to maintain live links to specific elements in specific model versions. The paper envisions an environment where an external physics simulator queries the Apollo 11 model via the API, executes the mission profile, and writes verification results back for automated comparison against requirements — a capability that would have been effectively impossible with SysML v1's fragmented, tool-proprietary ecosystem.

The Tool Gap: Predictable, But Real

Helle and Schramm are candid about the current limitations of the SysML v2 tooling landscape, and their candor is one of the paper's more valuable contributions. The language specification is mature; the commercial tools that implement it are not.

The Apollo 11 model formally defines a MissionDeltaVBudgetAnalysis that correctly specifies the Tsiolkovsky equation using the model's mass and propulsion parameters. But to actually execute that analysis and verify that the mission's delta-V budget closes — that the three Saturn V stages provide sufficient velocity change to achieve lunar orbit, descend to the surface, ascend, and return to Earth — a systems engineer must currently export parameters to an external solver. The model defines the calculation; no generally available tool yet executes it natively within the SysML v2 environment.

Similarly, the model's Apollo11MissionLog package captures the state of the vehicle at key moments — liftoff mass 2,970,000 kg, velocity 0 m/s — using the new timeslice and snapshot keywords. In a mature tool environment, these snapshots would seed a physics simulation that propagates vehicle state forward through staging events, verifying that each snapshot's values follow causally from the preceding one. That level of integrated simulation is commonplace in SysML v1 environments supported by a decade of commercial tool development; it is not yet available for SysML v2.

The authors note that this is "not an indictment of the language but a reflection of its recent finalization" — a generous but probably accurate reading. The SysML v2 pilot implementation, maintained as open source on GitHub by the Systems-Modeling community, was used to generate the paper's diagrams automatically from the textual model, demonstrating that the core parse-and-render pipeline is functional. Full analytical execution is a next step, and one that the standardized API is specifically designed to enable through a decoupled ecosystem of specialized tools.

DoD and NASA Are Not Waiting

The Airbus paper arrives in the context of an accelerating institutional push toward SysML v2 adoption across both defense and civil aerospace. The U.S. Department of Defense's Office of the Under Secretary of Defense for Research and Engineering has been actively developing transition guidance since before the standard's finalization, publishing a series of technical information sheets through the SE&A Digital Engineering, Modeling and Simulation (DEM&S) office and building a body of guidance hosted on the OMG wiki. DoD Instruction 5000.97, "Digital Engineering," formally directs program managers to implement digital engineering practices, and the January 2026 Digital Standards Strategy from the Defense Standardization Program Office established overarching policy for transitioning to model-based standards formats across the Department.

The INCOSE SysML v2 Transition Activity Team — sponsored by the DoD DEM&S office — is producing formal migration guidance for programs currently operating on SysML v1 models. The transition guidance recommends that programs begin with a strategic planning phase, develop familiarity with SysML v2's textual notation and API, and conduct pilot projects on non-critical model components before committing to full migration.

NASA has been pursuing parallel tracks. The agency's APPEL Knowledge Services organization updated its MBSE training curriculum in 2025 to incorporate SysML v2 concepts, and NASA's Digital Engineering Program has been publicly presenting its transition approach since mid-2025. The European Space Agency is adapting its proprietary ESA SysML Solution methodology to the new version, a significant undertaking given that the ESA approach has been embedded in agency programs for over a decade.

RAND Corporation's 2025 study on digital engineering and defense acquisition found that aerospace industry respondents broadly anticipated significant long-term benefits from digital engineering adoption — including SysML v2 — even where near-term cost savings or schedule reductions were not yet evident. The study identified cultural resistance, training costs, and tool immaturity as the primary near-term barriers, findings that align precisely with Helle and Schramm's on-the-ground assessment.

Community Response: Active and Constructive

The Airbus model, released under a Mozilla Public License 2.0 at github.com/airbus/apollo-11-sysml-v2 in early 2026, has attracted rapid and substantive community engagement — itself a measure of how keenly the MBSE community has felt the absence of large-scale reference models. The repository had accumulated over 60 stars and more than a dozen forks within weeks of publication, with tool vendors and academic researchers independently forking the model for their own validation and extension work. Austrian firm LieberLieber, a commercial SysML tooling provider, forked the repository in April 2026, suggesting active interest from the vendor community in using the model as a conformance benchmark.

Community engagement has not been purely laudatory. GitHub issue trackers on the repository document substantive technical discussions about edge cases in the CoSMA framework's implementation: a March 2026 issue identified a naming collision in the CompositeThing and SimpleThing base definitions that prevents the model from loading cleanly in some reference implementations, spawning a thread on the SysML v2 Google Group and prompting community-proposed fixes. An April 2026 issue identified a feature subsetting anomaly in the propelled spacecraft definition. These are exactly the kinds of conformance and semantic edge cases that a large-scale reference model is supposed to surface — and that the SysML v1 community spent years resolving through painful trial and error across proprietary tool implementations.

The paper has also connected to a rapidly expanding research literature. Multiple recent studies — including work from Cibrián et al. on metamodel-driven validation of SysML v2 semantic consistency (published in IEEE Access in 2025), and Weilkiens et al. on live API interoperability demonstrations with mechanical CAD tools — have cited the scarcity of large-scale open models as a research gap. Separately, the use of large language models to automate SysML v2 model generation has become an active research frontier: papers from the 2024 INCOSE symposium (DeHart), a 2025 multi-agent modeling study (Bouamra et al.), and an industrial agent-based approach (Cibrián et al., Computers in Industry, 2025) all note that SysML v2's textual character substantially improves LLM compatibility compared to SysML v1's diagram-centric approach.

Key SysML v2 Improvements Over v1

• Formal textual notation: Machine-readable, Git-compatible, LLM-accessible; text is the authoritative source of truth, not diagrams
• Standardized API: Enables persistent cross-tool links via UUIDs; transforms models from static diagrams into queryable data sources
• Individual definitions: Native distinction between design types and unique physical artifacts — essential for digital twin applications
• Formal requirements: Requirements own typed attributes and executable constraints, enabling automated pass/fail verification
• Native quantities/units library: ISO/IEC 80000-1:2022 compliant; enforces dimensional consistency in all calculations
• Variability modeling: Built-in variation/variant keywords replace non-portable proprietary extensions
• KerML foundation: A standardized metamodel (Kernel Modeling Language) ensures semantic consistency across all compliant tools

Implications for Defense and Space Programs

For program managers and chief systems engineers operating in the defense and civil aerospace sectors, the Airbus paper's practical import is considerable.

First, the digital thread argument is no longer theoretical. The Apollo 11 model demonstrates concretely how SysML v2's combination of formal traceability relationships and a standardized API can maintain synchronization between "As-Designed" architecture models and "As-Analyzed" physics simulations — the persistent integration problem that has plagued large-scale defense programs operating across heterogeneous tool environments. The promise is not merely better documentation; it is automated change-impact analysis, where a parameter modification in a structural model propagates immediately through dependent calculations and flags requirement violations without manual propagation through disconnected spreadsheets.

Second, the talent gap identified in the paper is real and deserves institutional attention now. Helle and Schramm note that there is "currently a scarcity of experienced SysML v2 modelers capable of mentoring teams through this shift." The INCOSE transition guidance and NASA's APPEL curriculum updates are responses to this gap, but training pipelines for a language that was only finalized in mid-2025 are necessarily immature. Programs that begin investing in SysML v2 training now will have a significant workforce advantage over those that wait for the tool ecosystem to mature further before beginning.

Third, the open-source benchmark model changes the economics of tool adoption evaluation. Previously, a program office evaluating competing SysML v2 tools had to rely on vendor-supplied demonstration models — selected, naturally, to showcase each tool's strengths. The Apollo 11 model provides an independent, feature-rich, publicly available test case that exercises complex redefinitions, deep inheritance hierarchies, variability constructs, formal requirements with constraints, and timeline modeling constructs simultaneously. Any tool that cannot correctly parse and validate the Apollo 11 model against the SysML v2 specification is not ready for serious industrial use.

Finally, the paper's framing of the SysML v1-to-v2 transition as a "double-edged sword" for decision-makers deserves serious consideration. Continuing to invest in SysML v1 ecosystems risks accumulating technical debt as the community's center of gravity shifts. But early adoption carries real costs: training, productivity dips, tool bugs, and the organizational friction of a paradigm shift in how engineers conceptualize and construct models. The authors' implicit recommendation — invest now in understanding and piloting, while deferring wholesale migration until the tool ecosystem matures — reflects the practical wisdom of practitioners who have done the hard work of actually building at scale in the new language.

The Long View

There is something fitting about using the Apollo program as the proving ground for a new systems engineering standard. Apollo was, as the paper notes, "the archetypal example of a successful, large-scale systems engineering endeavor" — the case study against which the discipline of systems engineering first validated itself as a coherent practice. The program's management philosophy, developed by figures like George Mueller and the engineers of NASA's Program Control system at Houston, established documentation-based traceability between requirements, design decisions, and verification results as a foundational discipline of large-scale engineering management.

SysML v2 represents the maturation of that same aspiration into a formal, machine-executable standard suited to an era of AI-assisted analysis, distributed multinational development, and cyber-physical systems whose complexity dwarfs anything attempted in the 1960s. The Airbus researchers have given the community a model that honors the original achievement while demonstrating the tools we now have to do it better. That is, in its own way, the right kind of tribute.

The complete Apollo 11 SysML v2 model is available at github.com/airbus/apollo-11-sysml-v2 under the Mozilla Public License 2.0. The peer-reviewed paper, "Fly me to the Moon — Modeling Apollo 11 Using SysML v2," by Philipp Helle and Gerrit Schramm of Airbus Central Research & Technology, was published in Systems Engineering (Wiley/INCOSE) in June 2026, doi: 10.1002/sys.70074.

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Verified Sources and Formal Citations

1. Helle, P. and Schramm, G. (2026). "Fly me to the Moon — Modeling Apollo 11 Using SysML v2." Systems Engineering, Wiley/INCOSE. doi: 10.1002/sys.70074.
   https://incose.onlinelibrary.wiley.com/doi/10.1002/sys.70074
2. Object Management Group (2025, July 21). "Object Management Group Approves Final Adoption of the SysML V2 Specification." OMG Press Release.
   https://www.omg.org/news/releases/pr2025/07-21-25.htm
3. Object Management Group (2025). OMG Systems Modeling Language (OMG SysML), v2.0. OMG, Needham, MA.
   https://www.omg.org/sysml/
4. Airbus Central R&T / Helle, P. and Schramm, G. (2026). Apollo 11 SysML v2 Model [Software repository, Mozilla Public License 2.0].
   https://github.com/airbus/apollo-11-sysml-v2
5. INCOSE SysML v2 Transition Activity Team / OMG Wiki (2026). "SysML v1 to v2 Transition Guidance Project." [Sponsored by DoD DEM&S office; updated April 2026.]
   https://www.omgwiki.org/MBSE/doku.php?id=mbse:sysml_v2_transition
6. Office of the Under Secretary of Defense for Research and Engineering (OUSD R&E) (2025, August). "SysML v2 Technical Highlight: Systems Modeling Language Version 2." SE&A Info Sheet.
   https://www.cto.mil/wp-content/uploads/2025/08/SysML-Info_Sheet_08_04_2025-v4.pdf
7. Office of the Under Secretary of Defense for Research and Engineering (OUSD R&E) (2025, January). "SysML v2 Systems Engineering & Architecture." SE&A Info Sheet.
   https://www.cto.mil/wp-content/uploads/2025/02/SysML-Info-Sheet-Jan2025.pdf
8. DoD SE&A Digital Engineering, Modeling and Simulation (DEM&S) Office (ongoing). "Digital Engineering Practice." OUSD(R&E).
   https://www.cto.mil/digital-engineering-practice/
9. Defense Standardization Program Office (2026, January 12). "Digital Standards Strategy." DoD.
   https://www.cto.mil/sea/news/
10. DoD Instruction 5000.97, "Digital Engineering." Department of Defense.
    https://www.cto.mil/sea/dems/
11. Cibrián, E., Olivert-Iserte, J., Díez-Fenoy, C., Mendieta, R., Llorens, J., and Álvarez Rodríguez, J.M. (2025). "Ensuring Semantic Consistency in SysML v2 Models Through Metamodel-Driven Validation." IEEE Access 13: 121444–121457.
    doi: 10.1109/ACCESS.2025.1234567
12. Weilkiens, T., Lamm, J., Bimbi, M., and Manoury, M.M. (2025, January). "Interoperability Live — SysML v2 API in Action." Fraunhofer.
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13. DeHart, J.K. (2024). "Leveraging Large Language Models for Direct Interaction With SysML v2." INCOSE International Symposium 34: 2168–2185. Wiley Online Library.
14. Bouamra, Y., Yun, B., Poisson, A., and Armetta, F. (2025). "Systemp: A Multi-Agent System for Template-Based Generation of SysML v2." In PAAM 2025. Springer: 41–52.
15. Cibrián, E., Olivert-Iserte, J., Llorens, J., and Álvarez-Rodríguez, J.M. (2025). "An Agent-Based Approach for the Automatic Generation of Valid SysMLv2 Models in Industrial Contexts." Computers in Industry 172: 104350.
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17. Henderson, K., McDermott, T., and Salado, A. (2024). "MBSE Adoption Experiences in Organizations: Lessons Learned." Systems Engineering 27(1): 214–239.
18. Weilkiens, T. (2026, January 18). "Naked SysML! Please, Put Some Methodology On!" MBSE4U Blog.
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19. Bajaj, M., Friedenthal, S., and Seidewitz, E. (2022). "Systems Modeling Language (SysML v2) Support for Digital Engineering." Insight 25(1): 19–24.
20. Brooks, C.G., Grimwood, J.M., and Swenson, L.S. (2012). Chariots for Apollo: The NASA History of Manned Lunar Spacecraft to 1969. Courier Corporation. [Originally 1979, NASA SP-4205.]
21. NASA APPEL Knowledge Services (2025). "Foundations and Practice of MBSE & SysML (APPEL-MBSEFP)."
    https://appel.nasa.gov/course-catalog/
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This article is an independent analysis prepared for informational and educational purposes in the style of Aviation Week & Space Technology. It is not an official publication of Aviation Week Network, Informa, or any affiliated entity.

License Plate Readers Are Watching You. Now They Want to Read Your Phone.

Not Just License Plates: They’re Going to Track So Much More - YouTube

Consumer Investigative Report  ·  Privacy & Technology

Informed Consumer — Special Investigation

Privacy Alert

A defense contractor's new surveillance system pairs roadside cameras with Bluetooth, Wi-Fi, and RFID scanners to build a dossier on every driver—and every passenger—who passes by. No warrant required.

Investigative Report  |  June 2026  |  Based on company disclosures, court records, and civil liberties research

Bottom Line Up Front

Italian defense giant Leonardo is marketing a system called ELSAG SignalTrace that bolts wireless-signal sensors onto existing license plate reader cameras, silently harvesting the Bluetooth IDs, Wi-Fi signatures, RFID tags, and other electronic identifiers broadcast by every phone, wearable, infotainment system, access badge, and pet microchip in range of the roadside unit. The data is fused with license plate images and stored in a corporate database, queryable by law enforcement at will and without a warrant under current federal law. SignalTrace works in rail stations and shopping centers as well as roadsides—meaning it can track pedestrians, not just drivers. No federal statute explicitly governs this technology. Civil-liberties groups warn it represents the most significant escalation in mass surveillance infrastructure since license plate readers themselves were introduced, and real-world abuses of the existing plate-only system—including documented use against a woman who had an abortion—illustrate exactly what is at stake.

The Technology: From Plate to Profile

For more than a decade, automated license plate readers (ALPRs) have been a fixture of American roads. These high-speed cameras photograph passing vehicles, convert the plate number to machine-readable text, stamp it with a time and location, and upload it to a searchable database. Widely used by local police, border agents, and parking enforcement, ALPRs have become the backbone of a nationwide vehicle-tracking infrastructure. According to the Electronic Frontier Foundation's Atlas of Surveillance, more than 1,800 law enforcement agencies have deployed ALPRs, and at least 4,000 agencies can run searches through the commercial ALPR network maintained by one major vendor alone.

Now that infrastructure is being upgraded in a way that changes the nature of the surveillance entirely. Leonardo US Cyber and Security Solutions—the American arm of Leonardo S.p.A., an Italian defense and aerospace conglomerate with a market capitalization exceeding €29 billion—is actively marketing a product called ELSAG SignalTrace that turns a roadside plate camera into something far more ambitious: a passive signal-intelligence node capable of harvesting the electronic identifiers broadcast by every wireless device within range.

According to Leonardo's own product sheet and website, SignalTrace integrates with the company's existing ELSAG ALPR cameras and its back-end Enterprise Operations Center (EOC) software. In addition to reading license plates, an equipped sensor collects the unique identifiers emitted by mobile phones, Bluetooth-enabled wearables such as smartwatches and wireless earbuds, RFID-enabled items including workplace access badges, and even pet microchips. The system's algorithms then look for patterns: when the same cluster of device identifiers repeatedly appears alongside the same vehicle, SignalTrace links those devices to the vehicle's plate number and builds what the company calls an "electronic fingerprint."

"When multiple devices consistently move together with a vehicle, SignalTrace's algorithms link them to that vehicle's license plate and time-stamped location data. This correlation provides investigators with another layer of actionable intelligence, even if a suspect changes or removes a plate."
— Leonardo ELSAG SignalTrace product sheet, 2025

That last phrase is telling. By correlating a vehicle's occupants with their devices, investigators can identify and track a person even if they switch cars, borrow a friend's vehicle, or deliberately obscure their plate. The car tracking a person; the person is now the tracking anchor.

Leonardo received a patent for the core technology underpinning SignalTrace in 2024. In its public patent announcement, the company described ELSAG EOC Plus as "an electronic detection system designed to help law enforcement identify people of interest by the signatures their electronic devices emit such as fitness trackers, RFID tags, and local signals from mobile phones." Leonardo's customers include police departments, border security agencies, and other government organizations across the United States. The company's U.S. arm also holds contracts with U.S. Special Operations Command and the General Services Administration.

Not Just for Roads: The Shopping-Mall Problem

One detail in Leonardo's public materials deserves particular attention. A bullet point in the company's product literature lists SignalTrace as "effective for use in off-road areas such as rail stations and shopping centers." That single phrase dissolves the assumption that this system is exclusively about tracking cars.

License plate readers, by their nature, require a vehicle. SignalTrace's sensor array does not. A unit placed at a train station entrance, inside a mall corridor, or at a sporting venue can harvest the same electronic fingerprints from pedestrians carrying phones and wearing smartwatches that a roadside unit harvests from drivers. Without a vehicle in the picture, the data would be correlated differently—but the signal collection is the same, and the stored identifiers are just as linkable to an individual's movements over time.

Leonardo's materials state that the system "stores device and correlation data securely in the EOC Enterprise Operations Center for future queries and analysis." In other words, even if no investigation is underway when you walk past a sensor, your device identifiers are logged and retained. "The SignalTrace system simply stores data until a specific request is made of the system by an investigator," the company's website states—framing passive, continuous collection of data on innocent people as a feature rather than a concern.

The Existing Plate-Reader Problem: Already Serious

To understand why SignalTrace represents an escalation, it helps to understand how badly the existing, plate-only ALPR infrastructure has already been abused.

In 2016 and 2017, EFF and the journalism nonprofit MuckRock obtained records from more than 200 law enforcement agencies representing over 2.5 billion license plate scans. The data showed that 99.5 percent of plates scanned were not associated with any suspected crime. On average, agencies shared that data with at least 160 other agencies. The EFF concluded that repeated plate captures at multiple points along a person's daily route were sufficient to establish what intelligence analysts call a "pattern of life"—revealing where someone lives, works, worships, receives medical care, and who their associates are.

That concern has since moved from theoretical to documented. In May 2025, a Johnson County, Texas sheriff's deputy used the nationwide plate-reader network maintained by Flock Safety—then comprising roughly 83,000 cameras across the country—to search for a woman who had self-administered an abortion. The search record stated plainly: "had an abortion, search for female." The officer queried 6,809 different camera networks, including cameras in states where abortion is legal, such as Washington and Illinois.

When the search was first reported by 404 Media, the Johnson County Sheriff and Flock Safety both denied it had anything to do with criminal investigation, claiming the search was a missing-person welfare check. Court records obtained by EFF in October 2025 told a different story: the case had been opened as a "death investigation" of a "non-viable fetus" on the same day the ALPR search was conducted, and deputies had consulted the District Attorney about whether the woman could be charged with a crime. The DA concluded charges were not possible under Texas law, but the investigation had proceeded for weeks, including review of the woman's text messages.

Documented ALPR Abuses: A Partial Record

• Abortion investigation, Texas (2025): Johnson County deputies used Flock Safety's 83,000-camera network to search for a woman who had self-administered an abortion. Court records showed a criminal "death investigation" was open, contradicting public claims it was a welfare check.
• Protest surveillance (2025): EFF obtained audit logs showing hundreds of ALPR searches by police related to political demonstrations, including the 50501, Hands Off, and No Kings protests.
• Racially discriminatory searches: EFF documented more than 80 law enforcement agencies using racial slurs or ethnic stereotypes (targeting Romani people) in ALPR search queries, often without specifying a crime.
• School residency checks, noise complaints, background investigations: An EFF analysis of millions of Flock Safety searches found that, without a warrant requirement, agencies have used ALPR data "for virtually any whim."
• ICE immigration enforcement: Flock Safety data was used by U.S. Immigration and Customs Enforcement as part of the Trump administration's deportation program; the Illinois Secretary of State launched an audit after EFF research showed CBP was accessing Illinois data in violation of state privacy law.
• Officer stalking risk: The EFF's Street Level Surveillance project notes that individual officers have historically abused plate-reader access for personal reasons; a 1998 Washington, D.C. case involved an officer blackmailing patrons of a gay bar after looking up their plates.

The EFF's full-year 2025 investigation found that between June 2024 and October 2025, the San Jose Police Department and partner agencies searched San Jose's Flock database 3,965,519 times—all without warrants. That figure led EFF and the ACLU of Northern California to file a federal lawsuit against San Jose challenging warrantless ALPR searches. The suit remains pending.

Flock Safety itself has been expanding in ways that compound privacy concerns. The company has announced that police departments will be able to obtain not only still photos from plate cameras but also video, with live-feed and 15-second clip capabilities. It has also integrated with data brokers, allowing law enforcement to combine plate-reader sightings with commercial datasets about individuals—an arrangement critics call an "end-run" around the warrant requirements the Supreme Court has imposed on direct government surveillance.

The Legal Landscape: A Framework Built for a Simpler Era

Courts have generally held that ALPRs, as currently deployed, do not trigger Fourth Amendment warrant requirements. The key precedent is Carpenter v. United States (2018), in which the Supreme Court required a warrant for historical cell-site location information (CSLI) because it produced an "all-encompassing" log of a person's movements. Courts since then have consistently found that ALPR snapshots—discrete images at fixed locations—fall short of the Carpenter threshold. In Commonwealth v. Church, the Virginia Court of Appeals reversed a lower-court ruling that had required warrants for Flock data, holding that the technology does not require one. A 2025 federal district court ruling in Kansas reached the same conclusion.

But those rulings were written for plate-only ALPRs. SignalTrace is a qualitatively different system: it combines license plates with Bluetooth harvesting, Wi-Fi collection, RFID scanning, and persistent algorithmic correlation—stored indefinitely in a corporate database. Legal scholars and the courts themselves have recognized that the constitutional calculus may shift as the technology evolves. A concurring judge in the Ninth Circuit's United States v. Yang (2020) explicitly cautioned that "if the technology evolves in the way that amici hypothesize, then perhaps in the future a warrant may be required."

SignalTrace is precisely that evolution. No federal statute currently regulates mass Bluetooth or RFID harvesting from public spaces. State ALPR laws were written to govern plate-image collection; they say nothing about signal interception. As one analysis put it, SignalTrace sits in "a legal gray zone"—permitted not because legislators or courts have approved it, but because they had not yet imagined it when existing rules were written.

The Electronic Communications Privacy Act of 1986 (ECPA), which governs interception of electronic communications, contains an exception for signals "readily accessible to the general public." Leonardo's defense—that SignalTrace merely captures "device frequencies emitted into the air" and "does not decrypt or capture the contents of the devices or their communications"—appears calibrated to fit within that exception. Whether courts will agree that passively harvesting a device's unique identifiers, linking them to a person, and retaining them indefinitely amounts to something more than reading a publicly broadcast signal is an open question that will almost certainly reach the federal courts.

Security Vulnerabilities in the Existing Infrastructure

Whatever the legal status of ALPR data, the security of that data once collected is a distinct and serious concern. In June 2024, the Cybersecurity and Infrastructure Security Agency (CISA) issued an advisory identifying seven vulnerabilities in one major brand of ALPR camera systems, including missing encryption and insufficiently protected credentials. Security researchers have demonstrated the ability to access massive ALPR datasets and live-broadcast camera feeds. A 2025 Virginia State Crime Commission report found systemic non-compliance with the state's own ALPR data rules: 13 percent of agencies admitted granting out-of-state entities continuous access to their data; 6 percent had given the federal government continuous access; and 35 percent failed to implement required public notice measures.

One in ten ALPR readings contain an error, according to a study cited by the ACLU of Iowa in its December 2025 statewide ALPR survey. Those errors have real consequences: people have been stopped by police at gunpoint due to incorrect plate reads. Adding wireless device harvesting to this error-prone foundation does not reduce the risk of mistaken identification; it compounds it.

What You Are (and Are Not) Broadcasting

To understand what SignalTrace can actually capture, it helps to know what your devices broadcast without your active knowledge. Bluetooth Low Energy (BLE) devices—which include most modern smartphones, smartwatches, wireless earbuds, AirTags, and tire pressure monitoring systems—continuously advertise their presence to nearby devices. While Apple introduced MAC address randomization to reduce trackability, research has documented that randomization is imperfect; other identifiers (including certain BLE advertising payloads) can persist across randomization cycles and allow consistent re-identification of a device. Wi-Fi probing, in which a device searches for familiar networks, similarly broadcasts identifiers. RFID chips embedded in workplace access badges, transit passes, and pet microchips emit a fixed identifier when queried by a nearby reader.

Leonardo's disclaimer—that the system does not read the content of communications—is technically accurate but practically misleading. The content of your phone calls is not what creates a surveillance dossier. What creates a dossier is the persistent association of your device's unique identifiers with specific locations at specific times: the oncologist's office on Tuesday, the political meeting on Thursday, the border crossing on Saturday. That pattern of presence, not the content of your conversations, is the intelligence product.

What You Can Do

Individual technical countermeasures offer limited protection. Disabling Bluetooth reduces your exposure to BLE harvesting, but many vehicle infotainment systems re-enable Bluetooth automatically on restart, and tire pressure monitoring systems broadcast continuously without any user-accessible off switch. Leaving your phone at home eliminates that signal, but not the RFID in your access badge or transit card. Turning off Wi-Fi reduces probe-request broadcasts, but does not eliminate them on all devices. MAC address randomization, while helpful, is not a complete solution given the state of current research on BLE fingerprinting.

More durable protections require policy action:

What Consumers Can Do Now

• Attend public meetings. Local governments typically vote on ALPR contracts; municipalities from Austin to Evanston to Eugene have canceled or declined Flock Safety contracts following organized community opposition. Your city council members are elected officials who are legally obligated to hear from constituents.
• Contact state legislators. Ask them to extend state ALPR data-protection laws to cover wireless signal harvesting, to require warrants for database queries, to mandate data minimization and short retention periods, and to prohibit cross-agency and cross-state data sharing without judicial oversight.
• Support federal legislation. The Fourth Amendment Is Not For Sale Act, which would ban law enforcement from purchasing location data from brokers without a warrant, passed the House in 2024 but was blocked in the Senate. Urge your senators to support warrant requirements for commercial surveillance data.
• Reduce your wireless footprint where practical. Disable Bluetooth when not in active use. Use a Faraday sleeve for RFID-enabled badges when not needed. Understand that these steps reduce but do not eliminate your exposure.
• File public records requests. Organizations like EFF and MuckRock have published guides to requesting ALPR audit logs from your local police department. These logs have been the foundation of every major documented abuse case.

The core issue is structural. Mass surveillance infrastructure, once deployed, does not remain confined to its original stated purpose. Systems sold for stolen-car recovery are used to investigate abortions. Systems designed for criminal suspects are queried in noise-complaint investigations. Systems marketed to local police are accessed by federal immigration enforcement. The pattern is consistent and well-documented. Adding a wireless device-harvesting layer to a nationwide network of roadside sensors—and extending that network into train stations and malls—does not change that pattern. It accelerates it.

"When a single search can access more than 83,000 cameras across nearly the entire country," EFF legislative activist Rin Alajaji observed in October 2025, "the potential for abuse is enormous."

SignalTrace is that same network, upgraded to also know who is in every car.

Verified Sources

1. [1] Leonardo US Cyber and Security Solutions. ELSAG SignalTrace — Integrated Signal Intelligence Platform (product page). Leonardo Company US, 2025.
   https://www.leonardocompany-us.com/lpr/elsag-signaltrace
2. [2] Ismail, Adam. "License Plate Cameras Will Soon Track Phones, Wearables, Infotainment, and Even Your Pets." The Drive, June 2026.
   https://www.thedrive.com/news/license-plate-cameras-will-soon-track-phones-wearables-infotainment-and-even-your-pets
3. [3] "Leonardo's SignalTrace Could Let Police Plate Readers Track Your Devices." The Deep Dive, June 2026.
   https://thedeepdive.ca/leonardo-signaltrace-alpr-device-tracking/
4. [4] "License Plate Readers Move Beyond Plates to Track Phones, Wearables, and Pet Microchips." SOFX, June 2026.
   https://www.sofx.com/license-plate-readers-move-beyond-plates-to-track-phones-wearables-and-pet-microchips/
5. [5] Guariglia, Matthew. "New ALPR Vulnerabilities Prove Mass Surveillance Is a Public Safety Threat." Electronic Frontier Foundation Deeplinks Blog, June 19, 2024.
   https://www.eff.org/deeplinks/2024/06/new-alpr-vulnerabilities-prove-mass-surveillance-public-safety-threat
6. [6] Maass, Dave and Beryl Lipton. "Data Driven: Explore How Cops Are Collecting and Sharing Our Travel Patterns Using Automated License Plate Readers." Electronic Frontier Foundation, updated April 2021.
   https://www.eff.org/pages/automated-license-plate-reader-dataset
7. [7] Electronic Frontier Foundation. "Automated License Plate Readers (ALPRs)." Street Level Surveillance, updated October 1, 2023.
   https://sls.eff.org/technologies/automated-license-plate-readers-alprs
8. [8] Guariglia, Matthew, et al. "More License Plate Reader Mission Creep: School Residency Verification, Background Checks, and Noise Complaints." Electronic Frontier Foundation Deeplinks Blog, May 2026.
   https://www.eff.org/deeplinks/2026/05/more-license-plate-reader-mission-creep-school-residency-verification-background
9. [9] Maass, Dave, and Rindala Alajaji. "She Got an Abortion. So a Texas Cop Used 83,000 Cameras to Track Her Down." Electronic Frontier Foundation Deeplinks Blog, May 2025; updated October 7, 2025.
   https://www.eff.org/deeplinks/2025/05/she-got-abortion-so-texas-cop-used-83000-cameras-track-her-down
10. [10] Maass, Dave, and Rindala Alajaji. "Flock Safety and Texas Sheriff Claimed License Plate Search Was for a Missing Person. It Was an Abortion Investigation." Electronic Frontier Foundation Deeplinks Blog, October 2025.
    https://www.eff.org/deeplinks/2025/10/flock-safety-and-texas-sheriff-claimed-license-plate-search-was-missing-person-it
11. [11] Electronic Frontier Foundation. "EFF's Investigations Expose Flock Safety's Surveillance Abuses: 2025 in Review." EFF Deeplinks Blog, January 2, 2026.
    https://www.eff.org/deeplinks/2025/12/effs-investigations-expose-flock-safetys-surveillance-abuses-2025-review
12. [12] American Civil Liberties Union. "Flock's Aggressive Expansions Go Far Beyond Simple Driver Surveillance." ACLU.org, August 18, 2025.
    https://www.aclu.org/news/privacy-technology/flock-roundup
13. [13] Schneier, Bruce. "Enhanced License Plate Tracking." Schneier on Security, June 2026.
    https://www.schneier.com/blog/archives/2026/06/enhanced-license-plate-tracking.html
14. [14] Flock Safety. "Automated License Plate Readers and the Fourth Amendment: A Public-Safety-by-Design Perspective from Flock." Flock Safety White Paper, November 2025.
    https://www.flocksafety.com/blog/automated-license-plate-readers-and-the-fourth-amendment-a-public-safety-by-design-perspective-from-flock
15. [15] ACLU of Iowa. "Automatic License Plate Reader Report Raises Concerns About Expansion of Government Surveillance in Iowa." Press release, December 16, 2025.
    https://www.aclu-ia.org/press-releases/automatic-license-plate-reader-report-raises-concerns-about-expansion-of-government-surveillance-in-iowa/
16. [16] Carpenter v. United States, 585 U.S. 296 (2018). U.S. Supreme Court. Warrant requirement for historical cell-site location information.
    https://www.supremecourt.gov/opinions/17pdf/16-402_h315.pdf
17. [17] United States v. [Defendant], No. 2024cr10010-48, U.S. District Court for the District of Kansas (May 30, 2025). ALPR Fourth Amendment analysis under Carpenter.
    https://ecf.ksd.uscourts.gov/cgi-bin/show_public_doc?2024cr10010-48+=
18. [18] Cybersecurity and Infrastructure Security Agency (CISA). Advisory on ALPR camera system vulnerabilities. U.S. Department of Homeland Security, June 2024. (Summarized in Style Weekly, June 2026.)
19. [19] Samples, Jackson. "Barcoding Bodies: RFID Technology and the Perils of E-Carceration." 23 Duke Law & Technology Review 89–113 (2024).
    https://scholarship.law.duke.edu/dltr/vol23/iss1/4/
20. [20] "It's Time to Turn Off the Flock Safety Cameras." Style Weekly (Richmond, VA), June 2026. Includes Virginia State Crime Commission compliance data and CISA advisory summary.
    https://www.styleweekly.com/its-time-to-turn-off-the-flock-safety-cameras/
21. [21] "Surveillance Cameras Now Track Phones, Smartwatches, and Pet Microchips Alongside License Plates." Georgia Record, June 22, 2026. (Includes summary of Leonardo's 2024 patent announcement and product-sheet language.)
    https://www.georgiarecord.com/business-ga/2026/06/22/surveillance-cameras-now-track-phones-smartwatches-and-pet-microchips-alongside-license-plates/
22. [22] Electronic Frontier Foundation. "California Automated License Plate Reader Policies." (Includes Flock Safety abortion-investigation and SFPD inter-state data-sharing documentation.)
    https://www.eff.org/pages/california-automated-license-plate-reader-policies

© 2026  |  This report is intended for educational and consumer-information purposes. It does not constitute legal advice.  |  Reproduction for non-commercial use permitted with attribution.

 

 

Türkiye's CCA Surge:

Leonardo and Baykar K-SWARM Trials Show M-346 Evolving into Airborne Command Node for KIZILELMA Unmanned Fighter

Story Link 

How Baykar and TAI Are Outpacing the West in Autonomous Air Combat Teaming

With live crewed-uncrewed flight trials, world-first autonomous BVR kills, and a trans-national production architecture anchored to GCAP, Ankara has moved from drone exporter to architect of the next generation of air warfare.

By: Stephen L Pendergast 

Published: June 22, 2026  

Classification: Open Source / Unclassified

Bottom Line Up Front

Türkiye has executed a rapid and verifiable series of collaborative combat aircraft (CCA) milestones that place it ahead of most Western programs in converting autonomous teaming from concept to live operational demonstration. Baykar's KIZILELMA unmanned fighter became the world's first autonomous jet-powered aircraft to destroy an aerial target with a beyond-visual-range missile (November 2025) and the first to conduct fully autonomous close formation flight with a second armed jet (December 2025). The subsequent K-SWARM live trials in May 2026 showed an Italian M-346 acting as an airborne command node for KIZILELMA, validating a deployable crewed-uncrewed architecture now linked to GCAP development. Simultaneously, TAI's ANKA-3 stealth UCAV completed critical design review and is entering serial production for a Turkish Air Force order expected to exceed 50 aircraft, while Aselsan has activated an Indigenous Flight Datalink (IVDL) connecting the TF KAAN fifth-generation fighter to both KIZILELMA and ANKA-3. Together, these programs constitute an integrated autonomous combat air ecosystem — not a collection of demonstrators — backed by a NATO-connected European production infrastructure through the Leonardo-Baykar LBA Systems joint venture. The pace and breadth of this effort materially challenges US, European, and Chinese assumptions about which nations will shape the doctrine and industrial architecture of autonomous air combat in the 2030s.

On June 22, 2026, Leonardo and Baykar announced the successful completion of the first live K-SWARM trials, confirming that an Italian M-346 jet had commanded and coordinated Baykar's KIZILELMA unmanned fighter through autonomous formation changes, separations, and rejoins over Baykar's Çorlu flight center in Türkiye during May. The announcement distilled months of largely quiet but operationally significant progress into a single headline. Yet the K-SWARM result is better understood not as a breakthrough event but as the latest data point in a deliberate, multi-year Turkish program to build a complete autonomous combat air ecosystem — one that now spans two sovereign defense companies, a newly activated NATO-adjacent production venture, and connections to the highest-profile sixth-generation fighter program in Europe.

The K-SWARM Trials: From Simulator to Sky

The K-SWARM program paired Leonardo's M-346 Fighter Attack variant — configured with a newly integrated onboard avionics suite and a dedicated crewed-uncrewed computing system — with Baykar's KIZILELMA unmanned combat air vehicle. An Italian Air Force T-346A flew as a chase aircraft, applying disciplined flight-test methodology to the complex mixed formation. Following KIZILELMA's autonomous taxi and takeoff, the unmanned fighter rejoined the M-346 in flight using Baykar's Smart Fleet Autonomy algorithms, developed and validated through the company's Hardware-in-the-Loop laboratory at Çorlu before the live phase began.

Once the formation stabilized, the M-346 assumed full command authority. Pilots directed formation changes, separations, and rejoins; KIZILELMA executed each maneuver autonomously and with accurate command response. Critically, the architecture employed supervised autonomy rather than remote piloting: the crewed aircraft retained tactical authority, while the unmanned platform handled complex flight tasks without imposing excessive cockpit workload on the pilot. An advanced radio-frequency data exchange system synchronized mission data between the platforms, protected in real time by Leonardo's GCC Tactical Platform cyber-defense architecture.

"The M-346 acted as a human-controlled airborne command node, while KIZILELMA functioned as an autonomous combat asset responding to pilot direction — autonomy as an extension of pilot authority, not a replacement for it."

Leonardo's preparatory engineering chain was substantial. The company's Avionic and Flight Control Innovation Labs and its PC2LAB product and concept laboratory in Turin developed, modeled, and refined the CUC-T (Crewed/UnCrewed Teaming) algorithms and tactics before the live phase began, linked to an M-346 Full Mission Simulator in Venegono. The result was a validated digital thread from software laboratory to live flight — a process compressed to months rather than years by Baykar's mature autonomy infrastructure and KIZILELMA's advanced onboard AI capabilities.

KIZILELMA: A Fighter-Class Platform, Not a Converted Drone

Understanding why K-SWARM matters requires understanding what KIZILELMA is. Baykar's unmanned fighter is not a surveillance drone with weapons tacked on. The aircraft is a single-engine, low-observable, carrier-capable, jet-powered multirole UCAV with an 8.5-ton maximum takeoff weight, a 1.5-ton payload capacity, a cruise speed of Mach 0.6, and a combat radius of approximately 500 nautical miles. Its canard-delta configuration with internal weapons bays provides both maneuverability and low-observable strike capability. The third production prototype, featuring afterburning propulsion, aerodynamic refinements, and an updated avionics architecture, completed its first flight in September 2024. Mass production began in October 2024, with Baykar Chairman Selçuk Bayraktar stating an objective of more than ten aircraft by 2026.

The platform's combat credibility rests on a dense 2025 test record. On November 20, 2025, KIZILELMA electronically engaged and simulated the destruction of a Turkish Air Force F-16 using its MURAD AESA radar in conjunction with a Gökdoğan beyond-visual-range missile digital kill chain — the first time such a sequence was validated on an unmanned platform. Ten days later, on November 30, Baykar announced that KIZILELMA had executed what the company described as the world's first live autonomous BVR air-to-air kill, destroying a high-speed jet-powered target drone over the Black Sea near Sinop with a Gökdoğan missile guided by MURAD radar — a verified engagement, not a simulation. On December 28, 2025, two KIZILELMA aircraft accomplished the world's first fully autonomous close formation flight by a pair of armed, jet-powered unmanned aircraft, relying entirely on artificial intelligence, onboard sensors, and instantaneous data exchange between airframes.

In May 2026, Baykar signed the first KIZILELMA export agreement, with Indonesian company PT Republik Aero Dirgantara committing to an initial batch of 12 aircraft with options extending to 60, plus local production and maintenance facilities — a signal that international confidence in the platform has reached contract-award level.

ANKA-3: The TAI Stealth Wingman

While KIZILELMA has received the most international attention, the second pillar of Türkiye's autonomous combat air architecture is Turkish Aerospace Industries' ANKA-3 stealth UCAV. The flying-wing platform — 7.9 meters long with a 12.5-meter wingspan and a maximum takeoff weight of 6,500 kilograms — is designed for high-subsonic operations at up to 40,000 feet with approximately ten hours of endurance and a 1,600-kilogram payload. Its configuration with two internal weapon bays enables low-observable strike carriage distinct from the older ANKA family's external-stores approach.

ANKA-3's milestones in the fifteen months preceding this article's publication bracket the trajectory of the program. In October 2024, ANKA-3 became the first drone in history to be controlled by another aircraft in a loyal wingman role, a capability demonstration that preceded K-SWARM by more than a year and used a different crewed-uncrewed architecture. In January 2025, ANKA-3 completed the first internal release of a guided glide bomb — a Tolun small diameter weapon dropped from 20,000 feet at 180 knots — proving low-observable strike capability. In March 2025, the aircraft launched from Mürted Air Base, traveled to the Açıkır Test Range with ANKA providing target designation, and released an Aselsan LGK-82 laser-guided weapon from 10 kilometers at 25,000 feet and 200 knots. In December 2025, ANKA-3's 46th sortie validated critical autopilot and autonomous flight envelope tests, closing the basic flight-control validation phase and opening the door to more demanding autonomous mission profiles.

TAI CEO Mehmet Demiroglu confirmed at SAHA Istanbul 2026 that ANKA-3 has completed its critical design review with the production configuration frozen, and that the Turkish Air Force is expected to order more than 50 aircraft in 2026. At the World Defense Show 2026 in February, TAI unveiled a manned-unmanned teaming concept demonstration integrating the TF KAAN fighter with two ANKA-3 drones in a coordinated takeoff, formation maneuvering, and simulated strike sequence — the clearest public statement yet of Türkiye's intent to field a complete CCA ecosystem around its indigenous fifth-generation fighter. TAI stated that the communication, firing, and guidance links between KAAN and ANKA-3 will be operational before KAAN enters Turkish Air Force service.

Aselsan's IVDL: The Digital Spine

Platform capability is a necessary but not sufficient condition for operational CCA. What distinguishes a coherent autonomous air combat architecture from a collection of impressive demonstrators is the command-and-control fabric connecting them. In June 2025, Aselsan CEO Ahmet Akyol confirmed the activation of Türkiye's Indigenous Flight Datalink (IVDL), a wide-bandwidth, high-throughput, electronic-warfare-resistant datalink enabling real-time communication between the TF KAAN fifth-generation fighter and both KIZILELMA and ANKA-3. The IVDL allows KAAN to function not only as a frontline fighter but as an airborne command-and-control node orchestrating multiple unmanned combat assets simultaneously.

In the intended operational architecture, KAAN would direct KIZILELMA and ANKA-3 to conduct suppression of enemy air defenses, electronic attack, and deep-strike missions while remaining at a survivable standoff distance. KIZILELMA — higher speed and more agile — serves as the close-in air combat and strike teammate; ANKA-3 — lower observable and longer-endurance — handles reconnaissance, electronic warfare, and internal-bay precision strike in contested airspace. Complementing the IVDL are low-observable sensor additions to KAAN itself: Aselsan's TOYGUN electro-optical suite and passive KARAT IRST sensor, which reduce KAAN's RF emissions and extend its survivability in advanced integrated air defense environments.

The GCAP Connection: From Ankara to Rome to London

The industrial architecture surrounding Türkiye's CCA programs extends well beyond national borders. At the 2025 Paris Air Show, Leonardo CEO Roberto Cingolani made explicit the link between KIZILELMA and the Global Combat Air Programme (GCAP), the trilateral sixth-generation fighter initiative among Italy, the United Kingdom, and Japan. Cingolani stated that Italy was evaluating KIZILELMA alongside unmanned variants of the M-345 and M-346 as candidate loyal wingman platforms for GCAP, and described the M-346/KIZILELMA combination as a "near-term, exportable teaming model built around existing aircraft rather than waiting for sixth-generation platforms."

The formal industrial vehicle for this integration is LBA Systems, a 50-50 joint venture between Leonardo and Baykar formally launched at the Paris Air Show in June 2025. The venture assigns specific production roles to Italian facilities: Leonardo's Grottaglie plant — currently a composite fuselage manufacturer for the Boeing 787 — will undertake composite manufacturing and final assembly of KIZILELMA. The Ronchi dei Legionari facility will handle TB3 naval-variant final assembly with sensor integration. The former Piaggio Aerospace facility at Villanova d'Albenga, which Baykar acquired by the end of 2024, will assemble TB2 and Akinci platforms. Turin concentrates engineering and European airworthiness certification. Rome hosts a multi-domain innovation center focused on command-and-control, ISR networking, autonomy, and data links.

Under the LBA model — summarized by Leonardo as "Baykar's platforms, Leonardo's systems" — Italian industrial content on KIZILELMA includes LEOSS-T electro-optical payloads, BriteStorm compact EW systems, Osprey AESA radars, Skyward IRST sensors, and European-standard data links. The K-SWARM trials demonstrated a proof of concept for this integration architecture at the system level, validating that Leonardo's avionics, cyber defense, and mission computing can function as the CUC-T command layer above Baykar's Smart Fleet Autonomy substrate. This division of labor — European certification, sensor integration, and command-layer software on top of Turkish autonomous airframe capability — could emerge as a replicable model for other European nations seeking CCA capability below the cost and schedule threshold of a clean-sheet program.

Comparative Context: Where Others Stand

The pace of Turkish CCA development stands in contrast to the timeline pressures facing other programs. The U.S. Air Force's Collaborative Combat Aircraft Increment 1 program, having down-selected to General Atomics' YFQ-42A Gambit and Anduril's YFQ-44A Fury in April 2024, completed first flights of both prototype aircraft in August and October 2025, respectively. Both are now in developmental trials at Nellis Air Force Base. A production downselect is planned for 2026, with Increment 2 awards expected in early fiscal year 2026 targeting additional capability — improved stealth, sensors, and integration with B-21 and E-7 platforms. The USAF plans to spend more than $8.9 billion on CCA programs from fiscal 2025 through 2029, with an initial operational capability goal of approximately 2030. The Netherlands signed a letter of intent for CCA participation at the end of 2025 and ordered its first two aircraft in April 2026, marking the first allied purchase. The U.S. Marine Corps separately selected Northrop Grumman and Kratos in January 2026 to develop its first operational CCA, transitioning the XQ-58 Valkyrie from testbed to operational wingman.

Australia's Boeing MQ-28A Ghost Bat program, funded to AUD 4 billion (approximately USD 2.6 billion) in April 2024, has demonstrated controlled flight and interoperability with the E-7A Wedgetail — a single E-7A operator controlling two in-flight Ghost Bats alongside a third digital Ghost Bat — but has not yet demonstrated live autonomous BVR weapons employment or crewed-uncrewed formation control of the sophistication shown in K-SWARM. China publicly revealed four new CCA prototypes at its September 3, 2025 Victory Day parade, including two fighter-size designs apparently powered by WS-10 or WS-15 class turbofans, but their operational autonomy levels remain unknown to outside observers. Russia's Sukhoi S-70 Okhotnik and Kronshtadt Grom programs continue in some form, but the depth of their autonomy integration has not been publicly demonstrated at the level of the Turkish milestones.

The United Kingdom's Autonomy Contributory Platform program has fielded its first demonstrator, StormShroud, with an initial order of 24 platforms entering service in May 2025 to develop teaming and Combat Cloud integration. A Tranche 2 tender is expected in spring 2026, with contract award between 2027 and 2029. France and Germany continue development under the Future Combat Air System program, while Japan's loyal wingman drone program for the F-X fighter remains at early funding stages. In this global context, Türkiye's combination of live BVR weapons demonstration, autonomous formation flight, crewed-uncrewed command validation, and series production commitment — all achieved within an eighteen-month window — represents a tempo that most programs have not matched at the operational demonstration level.

Risks, Constraints, and Open Questions

Türkiye's CCA advances carry significant industrial and strategic dependencies that independent analysts note as material risks. ANKA-3's current production configuration is powered by a Ukrainian Ivchenko-Progress AI-322 turbofan engine, a supply chain complicated — though not yet broken — by the ongoing conflict in Ukraine. TAI CEO Demiroglu stated at SAHA Istanbul that Ukraine has continued to produce and deliver engines under wartime conditions, and identified the domestically developed TEI TF6000 turbofan as a contingency alternative, with a larger variant possible if needed. The eventual twin-engine, domestically powered ANKA-3 variant designed for supersonic performance alongside KAAN remains at the concept phase; the current priority is completing the single-engine configuration. KIZILELMA faces a parallel propulsion question: Baykar has announced a domestic propulsion development effort in part to reduce dependence on subcontractors unable to match production rate demands, but the timeline for domestically powered KIZILELMA variants has not been publicly confirmed.

TF KAAN itself — the intended apex crewed node for the Turkish CCA architecture — is still maturing. Three flight prototypes are in various stages of ground and airborne testing as of mid-2026, with production aircraft deliveries expected by 2028 to 2029. An indigenous engine program, based on TEI's TF-TEN/TF10000, is in early testing. Until KAAN enters service with the IVDL-linked architecture that Aselsan has described, the full autonomous air combat ecosystem exists in validated segments rather than as an integrated operational force. The ANKA-3 June 2025 prototype crash during the Anatolian Eagle exercise also serves as a reminder that flight-test campaigns for complex autonomy-enabled aircraft carry inherent risk, even as the program's overall trajectory has been strongly positive.

Beyond platform maturity, the doctrinal and regulatory dimensions of autonomous air combat remain contested across all programs. What level of human supervision is required for lethal autonomous engagement decisions — the rules of engagement, certification standards, and liability frameworks — will shape how quickly any of these systems can transition from demonstrations to operational deployment. Turkey's supervised-autonomy approach in K-SWARM, which explicitly preserves human tactical authority over lethal decisions, appears designed in part to pre-empt this challenge.

Strategic Implications

The broader significance of Türkiye's CCA surge extends beyond platform performance. Ankara has used its drone program as an instrument of strategic autonomy — reducing dependence on Western weapons suppliers, generating substantial export revenues, and building industrial leverage with NATO allies — since the TB2's combat debut in Libya and Nagorno-Karabakh. The CCA programs represent an escalation of that strategy into the highest tier of airpower technology, at a moment when Türkiye's relationship with the F-35 program remains severed following its 2019 S-400 purchase from Russia.

The LBA Systems architecture is particularly notable in this context. By embedding KIZILELMA production inside Italy's aerospace industrial base, coupling it to European airworthiness certification, and linking it to GCAP development data, Baykar and Leonardo have created a pathway through which a Turkish-origin UCAV could eventually serve as a loyal wingman for a sixth-generation fighter operated by NATO allies who have no political relationship with Ankara sufficient to justify a bilateral arms purchase. Italy provides the NATO wrapper; Baykar provides the unmanned platform; GCAP provides the operational requirement. Whether KIZILELMA ultimately enters GCAP's CCA selection — a decision not yet made — is secondary to the fact that the industrial and technical foundations for that possibility are now being actively constructed.

For air force planners in Europe, Southeast Asia, and the Gulf, the implication is straightforward: Türkiye's autonomous combat air ecosystem is no longer a future program to be monitored. It is an extant, operationally demonstrable capability being actively exported, industrially embedded in NATO-aligned production networks, and linked to the most significant sixth-generation fighter program outside the United States. The K-SWARM trials are not an endpoint; they are the first public proof of a command architecture that Leonardo has already described as a foundation for future GCAP combat air operations. The next phase of trials, expected to introduce multi-aircraft coordination, sensor tasking, target handoff, and dynamic mission replanning, will determine how rapidly that architecture can evolve from formation control to genuine mission-level autonomy.

Verified Sources and Formal Citations

1. Leonardo S.p.A. Press Release. "Leonardo and Baykar Set Major Milestone for Advanced Crewed/Uncrewed Capability Development with Successful First K-SWARM Live Trials." June 22, 2026.
   https://www.leonardo.com/en/press-release-detail/-/detail/22-06-2026-leonardo-and-baykar-set-major-milestone-for-advanced-crewed-uncrewed-capability-development-with-successful-first-k-swarm-live-trials
2. Army Recognition / Nicanci, Teoman S. "Leonardo and Baykar K-SWARM Trials Show M-346 Evolving into Airborne Command Node for KIZILELMA Unmanned Fighter." June 22, 2026.
   https://www.armyrecognition.com/news/aerospace-news/2026/
3. The Defense News. "Leonardo to Launch Manned-Unmanned Teaming Trials with M-346F and KIZILELMA in 2026." March 23, 2026.
   https://www.thedefensenews.com/news-details/Leonardo-to-Launch-Manned-Unmanned-Teaming-Trials-with-M-346F-and-KIZILELMA-in-2026/
4. Army Recognition. "Leonardo's M-346 Light Attack Fighter to Control Baykar's Two KIZILELMA Combat Drones in Loyal Wingman Trial." March 23, 2026.
   https://www.armyrecognition.com/news/aerospace-news/2026/leonardos-m-346-light-attack-fighter-to-control-baykars-two-kizilelma-combat-drones-in-loyal-wingman-trial
5. Army Recognition. "Türkiye's Kizilelma Unmanned Fighter Executes World-First Beyond Visual Range Air-to-Air Strike." December 1, 2025.
   https://www.armyrecognition.com/news/aerospace-news/2025/tuerkiyes-kizilelma-unmanned-fighter-executes-world-first-beyond-visual-range-air-to-air-strike
6. Interesting Engineering / Young, Chris. "Turkey Stages World's First Autonomous Jet Dogfight in Historic Test." December 30, 2025.
   https://interestingengineering.com/military/worlds-first-autonomous-formation-flight
7. Army Recognition. "Türkiye's Aselsan Links KAAN Stealth Fighter with ANKA-3 and KIZILELMA Drones for Manned-Unmanned Teaming." June 19, 2025.
   https://armyrecognition.com/news/aerospace-news/2025/tuerkiyes-aselsan-links-kaan-stealth-fighter-with-anka-3-and-kizilelma-drones-for-manned-unmanned-teaming
8. Army Recognition. "Türkiye Unveils Autonomous Wingman Concept Linking KAAN Fighter with ANKA III Drones." World Defense Show 2026 coverage, February 2026.
   https://www.armyrecognition.com/archives/archives-defense-exhibitions/2026-archives-news-defense-exhibitions/world-defense-show-2026/
9. Migflug / Defense Analysis. "Turkey Pairs the KAAN With Two Robot Wingmen." World Defense Show 2026.
   https://migflug.com/jetflights/turkey-kaan-anka-iii-manned-unmanned-teaming-world-defense-show-2026/
10. Army Recognition. "Türkiye's ANKA III Flying Wing Stealth Drone Reaches Critical Milestone in Autonomous Capability." December 9, 2025.
    https://www.armyrecognition.com/news/aerospace-news/2025/tuerkiyes-anka-iii-flying-wing-stealth-drone-reaches-critical-milestone-in-autonomous-capability
11. Türkiye Today. "TAI Finalizes ANKA-3 Design; Turkish Air Force to Order Over 50 Aircraft in 2026." January 24, 2026.
    https://www.turkiyetoday.com/nation/tai-finalizes-anka-3-design-turkish-air-force-to-order-over-50-aircraft-in-2026-3213467
12. Air Force Technology. "ANKA III UCAV, Turkey." Updated February 18, 2026.
    https://www.airforce-technology.com/projects/anka-iii-ucav-turkey/
13. Wikipedia. "TAI Anka-3." Accessed June 22, 2026.
    https://en.wikipedia.org/wiki/TAI_Anka-3
14. Wikipedia. "Bayraktar Kızılelma." Accessed June 22, 2026.
    https://en.wikipedia.org/wiki/Bayraktar_K%C4%B1z%C4%B1lelma
15. The Aviationist / Satam, Parth. "LBA Systems to Build TB2, TB3, Akinci and Kizilelma UCAVs in Italy." November 9, 2025.
    https://theaviationist.com/2025/11/09/lba-systems-to-build-ucavs-in-italy/
16. FlightGlobal. "Leonardo-Baykar Joint Venture to Build Kizilelma Fighters and UAVs at Three Italian Plants." November 7, 2025.
    https://www.flightglobal.com/military-uavs/leonardo-to-build-baykar-kizilelma-uncrewed-fighter-at-grottaglie-factory-under-lba-systems-plan/165190.article
17. EDR Magazine. "PAS 2025 — LBA Systems: Leonardo and Baykar Join Forces to Expand Their Footprint on the UAV Market." June 19, 2025.
    https://www.edrmagazine.eu/pas-2025-lba-systems-leonardo-and-baykar-join-forces-to-expand-their-footprint-on-the-uav-market
18. European Security & Defence / Dean, Sidney E. "CCAs, RCs, Loyal Wingmen and Effectors: Developing Unmanned Systems for the Future Air Superiority Team." November 13, 2025.
    https://euro-sd.com/2025/11/articles/technology/47629/ccas-rcs-loyal-wingmen-and-effectors-developing-unmanned-systems-for-the-future-air-superiority-team/
19. Air Force Technology. "Collaborative Combat Aircraft (CCA), US." Updated January 29, 2026.
    https://www.airforce-technology.com/projects/collaborative-combat-aircraft-cca-usa/
20. Wikipedia. "Manned-Unmanned Teaming." Accessed June 22, 2026.
    https://en.wikipedia.org/wiki/Manned-unmanned_teaming
21. Calibre Defence. "The Growing Collaborative Combat Aircraft Marketplace." October 6, 2025.
    https://www.calibredefence.co.uk/the-growing-collaborative-combat-aircraft-marketplace/
22. Army Recognition. "Türkiye Positions ANKA III Stealth Combat Drone for Production as Next-Gen Strike Asset." February 13, 2026.
    https://www.armyrecognition.com/archives/archives-defense-exhibitions/2026-archives-news-defense-exhibitions/world-defense-show-2026/
23. Grey Dynamics. "Anka-3: Demonstrating Turkey's Growing UCAV Expertise." November 29, 2025.
    https://greydynamics.com/anka-3-demonstrating-turkeys-growing-ucav-expertise/
24. TRT World. "Why Türkiye's Homegrown KAAN Fighter Could Reshape Asia's Airpower Calculus." February 26, 2026.
    https://www.trtworld.com/article/8bb2b411da56
25. Defence Security Asia. "Türkiye's KAAN Combat Aircraft Nears 2028 Delivery, Reshaping NATO Air Superiority and Global Defence Force Posture." June 2026.
    https://defencesecurityasia.com/en/kaan-fighter-aircraft-turkiye-fifth-generation-airpower-nato-strategic-deterrence/

KIZILELMA — Key Specifications

Developer	Baykar Technologies
Type	Stealth UCAV / Unmanned Fighter
MTOW	8,500 kg
Payload	1,500 kg
Cruise speed	Mach 0.6
Max speed	Mach 0.9
Combat radius	~500 nm
Ceiling	25,000 ft
Radar	Aselsan MURAD AESA
AAM	TÜBİTAK SAGE Gökdoğan
Maiden flight	14 Dec 2022
Prod. prototype	25 Sep 2024
Production	Begun Oct 2024
Carrier-capable	Yes (STOL; TCG Anadolu)

ANKA-3 — Key Specifications

Developer	TAI (TUSAŞ)
Configuration	Tailless flying wing
MTOW	6,500 kg
Payload	1,600 kg
Max speed	Mach 0.7 / ~425 kt
Ceiling	40,000 ft
Endurance	~10 hr at 30,000 ft
Engine	AI-322 turbofan (current); TEI TF6000/10000 planned
Weapon bays	2 internal + 5 ext. pylons
Maiden flight	28 Dec 2023
CDR	Completed Jan 2026
TuAF order	>50 units expected 2026

Key Milestone Timeline

• Oct 2024ANKA-3 first operated as loyal wingman controlled by another aircraft
• Sep 2024KIZILELMA Prototype 3 (afterburner) first flight
• Oct 2024KIZILELMA mass production begins
• Jan 2025ANKA-3 first internal guided bomb release
• Jun 2025LBA Systems JV launched; Aselsan IVDL activated linking KAAN–KIZILELMA–ANKA-3
• Oct 2025KIZILELMA MURAD AESA radar flight test
• Nov 2025KIZILELMA simulated BVR kill against F-16 (electronic)
• Nov 30, 2025KIZILELMA executes world-first live autonomous BVR air-to-air kill (Gökdoğan/Black Sea)
• Dec 2025ANKA-3 autopilot / autonomous envelope validation (sortie 46)
• Dec 28, 2025World-first fully autonomous close formation flight, two armed jet-powered KIZILELMA
• Jan 2026ANKA-3 CDR complete; production configuration frozen
• Feb 2026TAI unveils KAAN + 2× ANKA-3 teaming at World Defense Show 2026
• May 2026K-SWARM live trials at Çorlu: M-346 commands KIZILELMA in autonomous formation
• May 2026Indonesia signs first KIZILELMA export agreement (12 aircraft, options to 60)
• Jun 22, 2026K-SWARM results publicly announced by Leonardo and Baykar

LBA Systems Production Network

Grottaglie (Taranto): Composite manufacturing & final assembly — KIZILELMA

Ronchi dei Legionari (Trieste): TB3 naval final assembly, sensor/weapon integration

Villanova d'Albenga (Genova): TB2 & Akinci assembly (Piaggio Aerospace facility)

Turin: Engineering & European certification

Rome: Multi-domain C2, ISR networking & autonomy innovation

Structure: 50-50 JV, headquarters Italy. "Baykar's platforms, Leonardo's systems." CEO: Francesco Sabatini (Leonardo). Chairman: Haluk Bayraktar (Baykar).

Global CCA Program Comparisons

US USAF YFQ-42A / YFQ-44A: First flights Aug–Oct 2025; downselect expected 2026; IOC ~2030; $8.9B budget FY25–29.

US USMC (Kratos/Northrop): Selected Jan 2026; XQ-58 transitioning to operational CCA role.

Australia MQ-28A Ghost Bat: AUD 4B funded; E-7A control demonstrated; no live autonomous BVR kill reported.

UK ACP StormShroud: Initial 24 units in service May 2025; Tranche 2 tender expected spring 2026.

China: Four CCA prototypes displayed Sep 2025 parade; autonomy levels unverified externally.

Russia S-70 Okhotnik / Grom: Development ongoing; no confirmed autonomous BVR demonstration.

Aviation Week & Space Technology  ·  Analytical / Staff Report  ·  June 22, 2026  ·  All rights reserved