The AI Co-Pilot for theEMS Battlefield

A new generation of real-time AI tools now gives operational commanders what they have never had before:

AI-ENABLED ELECTROMAGNETIC SPECTRUM OPERATIONS • COMMANDER’S DECISION SUPPORT

A new generation of real-time AI tools now gives operational commanders what they have never had before: continuous machine-speed monitoring of the electromagnetic environment, intelligent threat attribution, and tactically reasoned recommendations for countering jamming and operating through degraded communications.

 • February 2026

BLUF — Bottom Line Up Front

The Army is fielding and prototyping a layered AI-enabled system that, for the first time, gives the operational commander a real-time picture of the electromagnetic battlespace, automated threat identification, and machine-generated courses of action for degraded operations. The key components — S2AS, TLS BCT Manpack, EWPMT-X, and the NGC2 common data layer with ML triage — are either in rapid fielding or scheduled for FY2026 demonstration and procurement. At the joint level, Palantir’s EMBM-J DS prototype brings AI-assisted COA analysis directly to joint electromagnetic spectrum operations planners. The overarching framework is the Army’s Next Generation C2 initiative, whose explicit design goal is enabling human decisions at machine speed in a denied, degraded, intermittent, and limited (DDIL) environment. The capability exists — the challenge is integration, classification-level interoperability, and building the command culture to act on AI recommendations without waiting for certainty that will never come.

THE COMMANDER’S PROBLEM

Until very recently, the operational commander’s awareness of the electromagnetic environment was a manual, intermittent, specialist-dependent process. The CEMA officer brought a picture to the commander during the intelligence update brief. The spectrum manager worked from spreadsheets. Jamming was identified when communications failed — after the fact, by inference, not by detection. The OPFOR at NTC Fort Irwin described the result precisely: Blue Force commanders, deprived of their common operating picture, froze. They did not know what was happening in the spectrum, could not distinguish jamming from equipment failure or interference, and could not make a confident decision about whether to fight through degradation or transition to an alternate communications tier.

The problem has three distinct layers. The first is monitoring: the commander needs a continuous, visual, machine-speed picture of the electromagnetic operating environment (EMOE) — what signals are present, whose they are, what the friendly emission signature looks like, and where anomalies suggesting jamming, spoofing, or adversary reconnaissance are appearing. The second is threat detection and attribution: the system must identify not just that something is happening in the spectrum, but what it is, who is likely doing it, and what operational intent it signals. The third is decision support: given a characterized threat and a current mission, the system must recommend actionable responses — maneuver options, communication tier changes, electronic attack targeting, or emission control measures — and express those recommendations in terms the commander can act on without a specialist translator.

Each of these layers now has a corresponding Army program in development or fielding. None of them is fully mature. But for the first time, the architecture to solve all three exists, is under test, and is on a fielding timeline. Understanding what each layer does — and where the current gaps are — is essential for any analysis of where AI truly transforms the commander’s EMS decision calculus.

LAYER 1 — MONITORING: S2AS AND THE REAL-TIME SPECTRUM PICTURE

The Spectrum Situational Awareness System (S2AS) is the foundational monitoring tool. Developed by Product Manager Electronic Warfare Integration under PEO IEW&S and built on mature commercial off-the-shelf technology, S2AS provides commanders with a real-time electromagnetic spectrum situational awareness display integrated directly into the command post’s C2 environment. It senses, detects, and reports emitter activity across the battlespace, visualizes friendly signal signatures — including those a unit may not know it is emitting — and identifies sources of electromagnetic interference, whether from coalition equipment, inadvertent fratricide jamming, or adversary systems.

The system is designed to work in conjunction with EWPMT-X, the Army’s next-generation electromagnetic warfare planning and management tool, so that what S2AS senses flows directly into the planning tool the CEMA cell and commander use to make decisions. Maj. Cedric Harris, assistant product manager for EW Integration, described the combined effect: “Modernizing spectrum awareness capabilities will enhance commanders’ ability to visualize and control the electromagnetic environment, enabling more effective decision making and improving protection to win the fight for spectrum dominance.”

S2AS completed a three-week User Assessment in August 2025 with soldiers from the 25th Infantry Division, 101st Airborne Division, 11th Airborne Division, and Special Operations Command — units across the operational spectrum, not just EW specialists. An operational demonstration test event was planned for Q1 FY2026, with a rapid fielding decision targeted for Q2 FY2026. The system is available in handheld and vehicle-adaptable configurations, meaning spectrum awareness can extend from the brigade command post down to dismounted company-level operations. This is the first time organic spectrum visualization has been available to maneuver formations at that echelon.

Critically, S2AS addresses one of the most dangerous NTC diagnostic findings directly: commanders who do not know they are being jammed because no one is watching the spectrum in real time. Col. Leslie Gorman, Army Capability Manager for EW, described the requirement as being able to “see yourself in the spectrum” — understanding what the unit’s own emission signature looks like to an adversary, and detecting when that signature is being exploited. Kenneth Strayer, product manager for EW and Cyber, extended the point: “Our systems also provide the ability to identify threats that are going to keep units from being able to communicate.” That sentence describes a fundamental shift from reactive to predictive spectrum management.


“Commanders will be able to see how they look in the spectrum and allow them to reposition or mask their own capabilities — or limit their EMS signature — while maintaining the ability to communicate.”
— Kenneth Strayer, PM EW & Cyber, PEO IEW&S

LAYER 2 — DETECTION: TLS BCT MANPACK, TEWS, AND ML SIGNAL CLASSIFICATION

Monitoring produces a picture. Detection produces an assessment. The TLS BCT Manpack — the Terrestrial Layer System Brigade Combat Team variant, now in rapid fielding to BCTs — provides the sensing capability that turns raw spectrum data into characterized threats. It combines signals intelligence collection, electronic attack, and direction finding in a manpack-portable system that enables brigades to identify adversary emitters, geolocate them for targeting, and begin electronic attack — all from a platform small enough to accompany a dismounted force.

What makes TLS BCT operationally relevant for the AI decision-support problem is its integration of machine learning signal recognition software. The system does not merely report that an unknown signal is present; it classifies the signal against known emitter libraries, assigns confidence levels to its attribution, and passes that structured intelligence into the NGC2 data layer where it can be correlated with other sensor feeds and acted upon. The Tactical Electronic Warfare System (TEWS), fielded to infantry BCT formations, provides the same ML-enabled signal recognition at the infantry echelon, ensuring that EW intelligence is not confined to armored or heavy formations.

At the more advanced end, DARPA’s PROWESS program — the Processor Reconfiguration for Wideband Sensor Systems, under development by SRI International and USC — is building reconfigurable processors that reclassify and respond to RF signals within 50 nanoseconds. This is not a human-speed capability; it is machine-speed autonomous threat characterization operating far faster than any human analyst can process. PROWESS is designed specifically for the problem that has broken traditional EW systems: modern digitally programmable radars and waveform-agile jammers change their signatures so rapidly that a library-lookup system built on known threats becomes obsolete almost as soon as it is deployed. AI-driven classification that learns from data rather than matching against fixed libraries is the only response at that speed.

The practical result for the operational commander is a detection picture that is continuous, automated, and increasingly accurate against novel threats — rather than dependent on a specialist analyst manually reviewing intercepts. At Project Convergence Capstone 5 in March 2025, the Army demonstrated that S2AS and TLS Manpack could feed adversary and friendly electromagnetic emissions data simultaneously into the NGC2 common data layer, providing a unified EMS picture that the division commander’s staff could see and act on. This was described by program officials as a proof of principle for the full operational vision.

System EMS Function AI/ML Component

• S2AS Real-time EMOE visualization, friendly signature monitoring, EMI detection Automated signal presence/anomaly alerts; EMI source attribution
• TLS BCT Manpack Brigade-level SIGINT collection, direction finding, EA ML signal classification against known/novel emitter libraries
• TEWS / TEWS-I Infantry BCT ES and limited EA ML signal recognition and IC-integrated signal detectors
• EWPMT-X / TAK-X Commander’s C2 tool for EMS planning, modeling, EA management ML-fused SIGINT; COA modeling; effects simulation; NG C2 integration
• EMBM-J DS (Palantir) Joint-level COA analysis, scheme of maneuver EMS risk scoring AI-automated joint EMS planning; COA scoring; wargaming engine
• PROWESS (DARPA) Autonomous RF signal classification and waveform adaptation Real-time ML reconfiguration at 50-nanosecond speed

LAYER 3 — DECISION SUPPORT: EWPMT-X, EMBM-J, AND AI-DRIVEN COA RECOMMENDATION

Monitoring and detection are necessary but not sufficient. The commander’s EMS decision-support gap is not primarily informational — it is cognitive. Even with a real-time spectrum picture and accurate threat attribution, the translation from “we are being jammed on this frequency by a probable Krasukha-class system at this grid” to “here is what you should do about it, in priority order, with tradeoffs” requires analytical work that currently sits with CEMA specialists. AI is the mechanism to automate that translation at machine speed.

EWPMT-X, the Army’s modernized electromagnetic warfare planning and management tool now migrating to the Tactical Assault Kit (TAK) framework for cross-service commonality, is where that analytical translation is embedded. Capability Drop 4 of the earlier EWPMT already added machine learning and AI tools that take raw SIGINT gathered across the battlefield and produce structured intelligence for EW cadres. EWPMT-X extends this substantially: it provides planning, modeling, and managing capabilities for multiple EW assets, simulates effects of potential electronic attack options, receives lines of bearing and sensor data from TLS and S2AS to produce EMOE visualizations, and — critically — synchronizes EW and spectrum management operations across intelligence, operations, and cyberspace for Multi-Domain Operations.

The EW Arsenal, a proof-of-concept knowledge repository demonstrated at Project Convergence Capstone 5, is designed as an adjunct to EWPMT-X that brings EW capabilities into the same conceptual framework that commanders use for kinetic fires. Bret Eddinger, Senior Engineer for Offensive EW at PEO IEW&S, explained the intent: “When we say ‘guns or bullets,’ the Army has a sense about what those things are. They know how to characterize the impacts of those munitions on targets. But what we need to realize is that electronic warfare is just another form of fire.” The EW Arsenal aims to give the commander and CEMA staff access to characterized EW effects against specific target types, rated by confidence and suppression duration, in the same mental model as artillery fire planning. An AI assistant that can propose “employ TEWS EA on this emitter to suppress for 15 minutes while you maneuver through this corridor” is a decision-support interface the combat arms commander can actually use without a CEMA translator.

At the joint level, the Palantir Electromagnetic Battle Management – Joint Decision Support (EMBM-J DS) prototype, awarded by DISA in March 2024 under a $9.8 million OTA agreement, represents the most explicitly AI-driven EMS decision-support tool in current development. Built on Palantir’s AIP (Artificial Intelligence Platform) at Impact Level 6, it ingests component-level courses of action and schemes of maneuver and evaluates the associated electromagnetic spectrum opportunities and risks against each. It produces COA scoring, supports wargaming of EMS effects, and automates key operational electromagnetic spectrum planning processes that currently require hours of manual staff work. EMBM-J DS Program Manager Betsy Park described it as “a first-of-its-kind planning tool that can automate key operational electromagnetic spectrum planning processes” — a description that, for an EMS-blind Army staff, is genuinely transformative.


“The ability to ingest component-level courses of action and schemes of maneuver into an overall joint plan and evaluate the associated electromagnetic spectrum opportunities and risks will be a significant technological leap forward for EMS operational planners.”
— Betsy Park, EMBM-J Program Manager, DISA PEO Spectrum

NGC2 AS THE INTEGRATING ARCHITECTURE

Each of the tools described above is powerful in isolation. What makes them operationally decisive is integration — and that is precisely what the Army’s Next Generation Command and Control (NGC2) initiative is designed to provide. NGC2 is not another system; it is a data-centric architecture for how all data, including EMS data, flows from sensor to decision-maker. Its design goal, as articulated by Kaloostian, director of the C2 cross-functional team within the new Transformation and Training Command, is to enable commanders to do “more, better, faster” in a DDIL environment where the old network architecture — heavy, slow to set up, and oblivious to spectrum signature management — would fail.

The NGC2 technology stack runs from a transport layer through an integration layer to a data layer to an application layer. It is the integration layer that is directly relevant to the EMS decision-support problem: this is where streams of information — S2AS detections, TLS intercepts, EWPMT-X planning data, intelligence feeds, friendly force positions — are fed into ML tools that triage and curate them before commanders interact with the data layer. The result, when working as designed, is a common operating picture in which EMS information is not a separate specialist domain but a visible layer in the same map interface the commander uses for maneuver, fires, and sustainment.

The Lightning Surge 1 exercise at Schofield Barracks in January 2026, conducted with the 25th Infantry Division, demonstrated the first operational proof of this integration. The Lockheed Martin-led NGC2 prototype — using a common data layer built by Raft with AI tools developed by Accelint — enabled the division to simultaneously visualize adversary and friendly electromagnetic emissions, share battlefield graphics and soldier positions, and process AI-generated spot reports using natural language voice and chat interfaces. The Army’s own after-action description identified “integrating and visualizing adversary and friendly electromagnetic emissions” and “accelerating commander updates using an AI pipeline to automate radio reporting” as two of the three primary assessed functions. EMS data was not a specialist annex; it was a first-class data stream in the division C2 picture.

The competing NGC2 prototype — Anduril’s development with the 4th Infantry Division, tested through the parallel Ivy Sting series at Fort Carson — provides a complementary picture. The Ivy Sting III exercise in December 2025 demonstrated AI models reviewing sensor data to rapidly recognize, process, and nominate targets for the fires kill chain. The application layer presents this to the commander as a series of decision applications covering intelligence, maneuver, fires, protection, sustainment, and information advantage, plus an operational modeling tool that generates courses of action using machine learning based on available data. The EMS picture flows into the same interface, alongside kinetic options, as one unified decision space.

THE DEGRADED ENVIRONMENT USE CASE: AI WHEN THE NETWORK IS FAILING

The most operationally demanding — and most important — use case for AI decision support is not when communications are working but when they are failing. The NTC repeatedly documents the catastrophic impact of commander cognitive paralysis when the common operating picture disappears under jamming. AI that can only function on a robust network is precisely wrong for this problem. The relevant question is what the system can do when bandwidth is degraded, nodes are jammed off, and the commander’s first-tier communications are denied.

NGC2’s architecture explicitly addresses this. At the NetModX experimentation event in September 2024, the Army tested the NGC2 architecture’s first experiment in a degraded environment — specifically examining how the system would function under a robust network, a resilient network, and an “intelligent, threat-informed network.” The concept of the threat-informed network is critical: an AI layer that monitors the spectrum, detects degradation, attributes it to probable cause (jamming versus interference versus equipment failure), and recommends network adaptation — routing around jammed nodes, switching waveforms, reducing emission signatures — automatically, without waiting for human analysis to complete.

L3Harris’s NGC2 contributions specifically address EW-contested communications continuity. Their architecture integrates multiple waveforms — TSM, Wraith, ARROW, MUOS, LDD, and FH3 — with AI-enabled adaptive gateway routing that automatically selects the available transport modality as the spectrum environment changes. The system provides waveform redundancy, dual-channel cross-banding, and iterative software-upgradeable countermeasures ensuring that the C2 network degrades gracefully rather than failing catastrophically when the first tier is jammed. The NGC2 Gateway Manpack, delivering up to 50 Mbps on a scalable mesh network with up to 800 nodes, is specifically designed to sustain mission command applications under disrupted conditions.

The S2AS contribution to degraded operations is equally direct. By detecting jamming at the moment it begins — not after communications have already failed — S2AS gives the commander a lead time window to act before cognitive paralysis sets in. If the AI system identifies a jamming signal consistent with Krasukha-class barrage jamming on the primary command net and immediately surfaces “Switch to PACE Contingency: SATCOM alternate; suppress emitter at [grid] with TEWS EA; reduce friendly CP emission profile” as a recommended action, the commander’s decision is reduced from open-ended analysis under stress to confirmation or rejection of a specific recommendation. That is the difference between paralysis and action.

The DDIL Scenario: What AI Decision Support Looks Like in Practice

0614 local. BCT command post detects sudden degradation on primary VHF command net. S2AS simultaneously alerts: new wideband emission detected, bearing 073 degrees, signal characteristics consistent with adversary EW system library entry KR-4 (Krasukha-class). Confidence: HIGH. TLS Manpack confirms direction finding fix. EWPMT-X automatically generates three recommended COAs ranked by mission impact: (1) Transition primary C2 to SATCOM alternate, suppress emitter with TEWS EA, maintain mission timeline — risk: exposes TEWS position; (2) Reduce CP emission profile, transition to burst-transmission protocol, request TLS EAB support from division — risk: 12-minute delay in fire mission; (3) Maneuver CP 800m to terrain mask, re-establish primary net from defilade — risk: 25-minute C2 gap. Commander selects COA 1. S2AS continues monitoring. Total elapsed time from jamming detection to commander decision: 90 seconds. Previous baseline without AI support: 8–15 minutes, if diagnosed correctly at all.

PACIFIC COMMAND’S AI SPECTRUM MANAGEMENT: THE INDOPACOM MODEL

Beyond the Army’s organic programs, U.S. Pacific Command (INDOPACOM) has already demonstrated what AI-enabled spectrum management looks like at the combatant command level — and the contrast with the prior baseline is stark. Bob Marcial, chief spectrum manager at INDOPACOM, described the old approach at TechNet Indo-Pacific 2025: “Right now, what we have are a lot of Excel spreadsheets, but AI is helping us do real-time spectrum management.” That sentence captures the before-and-after succinctly. The three AI-driven elements Marcial identified as transforming INDOPACOM’s spectrum operations are predictive pattern modeling of the EMS, real-time conflict detection and deconfliction, and waveform-adapting capabilities for interoperability with joint coalition partners.

Predictive pattern modeling is particularly relevant to the commander’s threat detection problem. An AI system trained on historical spectrum data from a given operational area can identify anomalous emission patterns — emissions that do not match known friendly or civilian signatures, that appear on frequencies associated with adversary EW doctrine, or that correlate temporally with known adversary operational activity cycles. This transforms threat detection from reactive (responding after jamming has begun) to anticipatory (identifying probable pre-jamming reconnaissance activity and alerting the commander before the attack).

THE DIGITAL ENEMY COMMANDER: AI RED-TEAMING THE EMS

A distinct but operationally powerful AI application sits at the intersection of threat detection and decision support: the use of AI agents to model adversary EW commander decision-making. The Army’s Military Intelligence Professional Bulletin published an analysis in mid-2025 describing this concept — AI agents trained on comprehensive datasets of adversary behavior, doctrine, EW communications, and decision-making patterns that can function as “digital enemy commanders.”

For EMS operations, this means an AI agent that models how a Russian EW battalion commander would respond to observed friendly force actions — what jamming assets would be activated, on what frequencies, at what point in the maneuver timeline, and with what intended effect. The intelligence officer can query this agent: “Given our planned Phase 2 movement through grid 47U, what EW response does the adversary 79th Separate Reconnaissance Brigade’s organic EW element most likely execute?” The agent’s response incorporates known Russian EW doctrine, observed signatures from prior contacts, and pattern analysis of adversary decision cycles.

Integrated with S2AS and EWPMT-X, this digital adversary modeling creates a closed prediction-confirmation loop: the AI predicts adversary EW actions based on friendly force activity; S2AS detects whether predicted emissions appear; EWPMT-X updates COA recommendations based on prediction accuracy. Over time, the model improves against the specific adversary unit the brigade is facing, not just against generic doctrine. This is the electronic warfare equivalent of what the 18th Airborne Corps’ AI-enabled targeting demonstrated for fires — dramatically increased precision and reduced cycle time against a specific, characterized threat.

THE RISKS: OVER-AUTOMATION, DATA POISONING, AND THE TRUST PROBLEM

The capabilities described above are real, are fielding, and represent a genuine transformation of the commander’s EMS decision problem. They also carry specific risks that any serious analysis must address.

The first risk is automation bias — the well-documented tendency of human decision-makers to over-trust AI recommendations, especially under stress. A commander who has been trained to act on EWPMT-X COA recommendations without scrutiny is vulnerable to a single point of failure: if the system’s threat classification is wrong — misidentifying friendly interference as adversary jamming, or mistaking a new commercial waveform for a threat emitter — the commander acts on bad intelligence at machine speed. Georgetown’s Center for Security and Emerging Technology flagged this specifically in its April 2025 analysis of military AI decision-support systems, noting that large language model-based systems can “confidently present incorrect information” and that skewed or scarce training data directly degrades battlefield recommendation quality.

The second risk is adversarial data manipulation. Russia is already using AI-based spectrum analysis tools against Ukrainian communications, including techniques designed to manipulate the electromagnetic environment in ways that exploit adversary classification systems. A sophisticated adversary who understands that the U.S. Army’s TLS BCT Manpack classifies signals against a known emitter library could, in principle, transmit spoofed signals calibrated to trigger specific AI classification outcomes — inducing the commander to take a predicted response that the adversary has planned for. This is a new vulnerability that did not exist when EW classification was done by human analysts applying judgment rather than by ML models applying pattern matching.

The third risk is the DDIL vulnerability of the AI system itself. An AI decision-support tool that requires cloud connectivity to function degrades in exactly the environment where it is most needed. The Army’s NGC2 design explicitly addresses this by emphasizing edge computing — ML tools that run on local compute within the unit’s data layer rather than requiring continuous cloud uplinks. But the residual capability of a disconnected AI decision-support system — what it can still do on local compute when the network is jammed and cloud sync is unavailable — is a critical parameter that must be defined, tested, and communicated to commanders before they encounter it in combat.


“The most transformative technology will have a bigger impact on future warfare than artificial intelligence. The goal is human decisions at machine speed.”
— Brig. Gen., Army NGC2 Program, December 2025

WHAT THE COMMANDER NEEDS TO DEMAND

Given this landscape, what should the operational commander demand from industry and program offices as AI-enabled EMS decision support matures? Three requirements stand out.

First, explainability. A COA recommendation that arrives without attribution — without a clear statement of what sensor data drove the threat classification, what confidence level the AI assigns to the emitter identification, and what doctrinal basis underlies the recommended response — is not actionable for a commander who needs to be able to override it. The EW Arsenal concept’s emphasis on characterizing EW effects the way artillery effects are characterized is the right model: the commander needs to understand what the AI is proposing to do and why, expressed in terms that do not require a CEMA specialist to interpret.

Second, graceful degradation specification. Every AI tool in this stack must have a documented and tested DDIL mode: what it does with partial sensor feeds, with degraded compute, with intermittent network connectivity. The commander must know in advance what the system can still do when 60% of its sensor inputs are offline — not discover it for the first time at 0300 during a contested river crossing.

Third, adversarial testing at realistic scale. TLS BCT Manpack, EWPMT-X, and EMBM-J DS have been tested in controlled exercises. They have not yet been tested against a sophisticated adversary running an active deception and spoofing campaign against the AI classification layer. The Army’s NGC2 2026 goal — testing the NGC2 prototype against enemy sensor capabilities — is the right next step. But it must include red teams specifically tasked with confusing and manipulating the AI components, not just stressing the network.

CONCLUSION: THE ARCHITECTURE IS READY; THE INTEGRATION IS THE WORK

The question Stephen asked — can AI systems help the operational commander monitor the electronic environment, detect threats, and recommend strategy and tactics to counter and operate in degraded conditions — has a clear and specific answer. Yes, and the tools are either in fielding or in FY2026 demonstration and procurement cycles. S2AS provides the real-time monitoring layer. TLS BCT Manpack and TEWS provide the ML-enabled detection and classification layer. EWPMT-X and the EW Arsenal provide the COA recommendation layer. EMBM-J DS provides the joint-level AI planning support. NGC2’s common data layer integrates all of them into a single picture that treats the electromagnetic battlespace as a first-class C2 domain rather than a specialist annex.

The gap is not technology — it is integration, tested DDIL performance, adversarial robustness, and the command culture change required to act on AI recommendations with the confidence and speed that closes the kill chain before the adversary’s 48-hour adaptation cycle makes the recommendation obsolete. Col. Gorman’s analogy is the right frame: owning the night took a generation of doctrine, training, and leader development after night vision goggles were invented. Owning the spectrum will require the same. The goggles are now being fielded. The work of building the Army that knows how to use them has barely begun.

SOURCES AND CITATIONS

S2AS — Spectrum Situational Awareness System

[1] PEO IEW&S. "Transformation in Contact Units Test Spectrum Situational Awareness System." August 20, 2025. https://peoiews.army.mil/2025/08/20/transformation-in-contact-units-test-spectrum-situational-awareness-system/

[2] Lawrence, Drew F. "Army Awards Prototype for Spectrum Sensing and Visualization Tool." DefenseScoop, April 2, 2025. https://defensescoop.com/2025/04/02/army-s2as-3db-labs-award-prototype-spectrum-awareness-system/

[3] Lawrence, Drew F. "Army Spectrum Tool Will Feature in Upcoming TiC Exercises to Inform Rapid Fielding Decision." DefenseScoop, February 24, 2025. https://defensescoop.com/2025/02/24/army-spectrum-tool-transforming-in-contact-rapid-fielding/

[4] Janes. "US Army to Field Key EW Programme by 2026." December 17, 2024. https://www.janes.com/osint-insights/defence-news/c4isr/us-army-to-field-key-ew-programme-by-2026

TLS, TEWS, EWPMT-X — EW Sensing and Planning Tools

[5] PEO IEW&S PM EW&C. Program Fact Sheet, 2025. https://peoiews.army.mil/pm-ewc/

[6] PEO IEW&S. "Fusing Intel and EW Data into the Army’s Data Centric NGC2 Architecture." December 23, 2024. https://peoiews.army.mil/2024/12/23/278296/

[7] PEO IEW&S. "PEO IEW&S Showcases the Future of Intelligence, EW and Surveillance at PC-C5." April 25, 2025. https://peoiews.army.mil/2025/04/25/peo-iews-showcases-the-future-of-intelligence-electromagnetic-warfare-and-surveillance-at-pc-c5/

[8] Lawrence, Drew F. "Army Alters Approach for Electromagnetic Spectrum Planning Tool." DefenseScoop, April 30, 2024. https://defensescoop.com/2024/04/30/army-alters-approach-electromagnetic-spectrum-planning-tool/

[9] Armada International. "Spectrum Battle Management." December 4, 2023. https://www.armadainternational.com/2023/12/us-army-ewpmt-moves-forward/

[10] Gorman, Col. Leslie. "Owning the Spectrum: How the Army Is Reinventing Electromagnetic Warfare." FedGov Today, September 11, 2025. https://fedgovtoday.com/fedgov-blogs/owning-the-spectrum-how-the-army-is-reinventing-electromagnetic-warfare

Palantir EMBM-J DS

[11] Palantir Technologies. "Palantir to Deliver Electromagnetic Battle Management – Joint Decision Support (EMBM-J DS) Prototype to DISA." March 29, 2024. https://investors.palantir.com/news-details/2024/Palantir-to-Deliver-Electromagnetic-Battle-Management---Joint-Decision-Support-EMBM-J-DS-Prototype-to-the-Defense-Information-Systems-Agency-DISA/

[12] Military Embedded Systems. "Electromagnetic Battle Management Prototype to Be Developed for DISA by Palantir." 2024. https://militaryembedded.com/radar-ew/rf-and-microwave/electromagnetic-battle-management-prototype-to-be-developed-for-disa-by-palantir

[13] The Defense Post. "DISA Orders Electromagnetic Battle Management Prototype From Palantir." April 4, 2024. https://thedefensepost.com/2024/04/03/us-electromagnetic-battle-management-prototype/

NGC2 — Next Generation Command and Control

[14] DefenseScoop. "How the Army Built Next-Gen Command and Control." March 20, 2025. https://defensescoop.com/2025/03/20/how-army-built-next-gen-command-and-control-ngc2/

[15] Federal News Network. "Inside the Army’s Push to Scale Next Gen C2 System and Prepare for AI." October 31, 2025. https://federalnewsnetwork.com/federal-insights/2025/10/inside-the-armys-push-to-scale-next-gen-c2-system-and-prepare-for-ai/

[16] Breaking Defense. "What’s Next for Army’s Ambitious Next Gen C2 Effort: 2026 Preview." December 31, 2025. https://breakingdefense.com/2025/12/whats-next-for-armys-ambitious-next-gen-c2-effort-2026-preview/

[17] Breaking Defense. "Army Tests Next-Gen C2 Data Layer for the First Time." January 23, 2026. https://breakingdefense.com/2026/01/army-tests-next-gen-c2-data-layer-for-the-first-time/

[18] Lockheed Martin. "Lockheed Martin Accelerates NGC2 Capabilities at Lightning Surge 1." January 23, 2026. https://news.lockheedmartin.com/2026-01-23-Lockheed-Martin-Accelerates-Next-Generation-Command-and-Control-NGC2-Capabilities-and-Real-Time-Decision-Making-at-Lightning-Surge-1

[19] Army.mil. "Army Teams with Industry to Refine AI Potential Supporting C2." December 14, 2025. https://www.army.mil/article/289556/army_teams_with_industry_to_refine_ai_potential_supporting_command_and_control

[20] DefenseScoop. "Data and Integration Will Be Core of Army’s Next-Gen C2." June 6, 2025. https://defensescoop.com/2025/06/06/data-and-integration-will-be-core-of-armys-next-gen-c2/

[21] L3Harris. "NGC2: Enabling Next Generation Command and Control." 2025. https://www.l3harris.com/all-capabilities/ngc2-enabling-next-generation-command-and-control

AI Decision Support: Risks and Frameworks

[22] Probasco, Emelia et al. "AI for Military Decision-Making: Harnessing the Advantages and Avoiding the Risks." CSET, April 2025. https://cset.georgetown.edu/publication/ai-for-military-decision-making/

[23] Williams, LTC Timothy J. "AI-Enabled Wargaming at the U.S. Army Command and General Staff College." Small Wars Journal, January 16, 2026. https://smallwarsjournal.com/2026/01/16/ai-enabled-wargaming-cgsc/

Cognitive EW and INDOPACOM Spectrum Management

[24] AFCEA SIGNAL. "Artificial Intelligence-Driven Components Helping Advance EMS Space." TechNet Indo-Pacific 2025. https://www.afcea.org/signal-media/artificial-intelligence-driven-components-helping-advance-ems-space

[25] USNI Proceedings. Lt. Cmdr. Brian P. Gannon. "Implement AI in Electromagnetic Spectrum Operations." August 2023. https://www.usni.org/magazines/proceedings/2023/august/implement-ai-electromagnetic-spectrum-operations

Digital Enemy Commander / AI Red Team

[26] Military Intelligence Professional Bulletin. "Know Thy Enemy: Using AI to Create Digital Enemy Commanders." July–December 2025. https://mipb.ikn.army.mil/issues/jul-dec-2025/know-thy-enemy/

PROWESS / Cognitive EW Research

[27] Air Force Tech Connect. "Artificial Intelligence and Machine Learning Aim to Boost Tempo of Military Operations." https://airforcetechconnect.org/news/artificial-intelligence-and-machine-learning-aim-boost-tempo-military-operations


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From the Mojave to the Donbas:

Army issues broad appeal to industry for electromagnetic spectrum solutions - Breaking Defense

ELECTRONIC WARFARE & SPECTRUM OPERATIONS 

The Army’s EMSO Overhaul and the Training Failures It Must Fix

The Army’s February 2026 Characteristics of Need document for electromagnetic spectrum operations is not an abstract acquisition reform. It is a direct response to documented training failures at NTC Fort Irwin — failures that Ukraine has proven will be fatal at operational scale.

SIGNAL Staff Report

WASHINGTON, D.C. • February 25, 2026

On February 25, 2026, the Army’s Program Executive Office for Intelligence, Electronic Warfare and Sensors released a broad Request for Information anchored in a Characteristics of Need document that describes, in plain institutional language, what the service cannot yet do in the electromagnetic spectrum. Responses are due March 13. But to read the document as a procurement notice would be to miss its deeper significance: it is, point by point, a formal acknowledgment of the shortcomings that have been exposed — rotation after rotation, year after year — at the National Training Center, Fort Irwin, California.

This article places the Army’s EMSO acquisition initiative in direct dialogue with those documented NTC findings, examining how each major element of the CoN maps onto specific failures observed when brigade combat teams face the 11th Armored Cavalry Regiment’s Opposing Force in the Mojave Desert. The connection is not incidental. The OPFOR’s consistent success against rotating units has generated the operational evidence base that now drives Army investment priorities, shapes the March 2025 EW Strategy, and has elevated electromagnetic warfare to a stated core competency of the Army Chief of Staff.

That same evidence base has been corroborated and sharpened by three-plus years of intensive combat in Ukraine, where Russian electronic warfare systems and tactics have demonstrated that the vulnerabilities exposed at Fort Irwin are not training artifacts — they are operationally lethal.

THE NTC DIAGNOSTIC: WHAT FORT IRWIN KEEPS REVEALING

Before examining the CoN in detail, it is useful to catalog the recurring failures the OPFOR’s Capt. Jake Thomas, Col. Kevin Black, and NTC rotation observers have documented publicly. They constitute a diagnostic portrait of a force unprepared for peer electromagnetic competition:

NTC FINDING 1 — Digital Dependency Collapse: Units over-reliant on high-bandwidth networks lose coherent command when jammed off primary comms — exactly what the OPFOR engineers.

NTC FINDING 2 — Cognitive Paralysis Without COP: When the common operating picture disappears, decision cycles slow fatally. Subordinate units commit piecemeal, enabling OPFOR defeat in detail.

NTC FINDING 3 — PACE Plan Inadequacy: Contingency and emergency communication tiers are undertrained and underequipped — “hopeful placeholders,” in one analyst’s phrase, not genuine fallback capabilities.

NTC FINDING 4 — Doctrinal-Cultural Gap: Combat arms officers don’t understand EMS vulnerabilities; signals soldiers don’t understand the battle plan. Neither can bridge the gap under contact.

NTC FINDING 5 — First-Jam-at-Fort-Irwin Problem: Training constraints mean the NTC rotation is often the first time soldiers experience jamming — while being graded, under time pressure, against a seasoned OPFOR.

NTC FINDING 6 — AI Disinformation Vulnerability: OPFOR uses generative AI to spoof commander voice orders and flood battle space with deepfake disinformation, exploiting trust in digital command channels.

NTC FINDING 7 — Drone RF Signature Exposure: Operators’ own control frequencies are intercepted by OPFOR, harvesting video feeds and GPS coordinates for precision targeting — mirroring Russian tactics in Ukraine.

NTC FINDING 8 — Fragmented, Non-Cohesive EW Systems: EW capabilities spread across warfighting functions with no common data architecture — preventing integrated employment and AI-enabled analysis.

NTC FINDING 9 — Expertise Too Thin and Too Senior: Institutional EW knowledge concentrated in a small number of specialists; impossible to replicate across the broader force at the required depth.

NTC FINDING 10 — Procurement Incapacity: After decades of Cold War divestiture, the Army fielded its first program-of-record ground jammer only in recent years — a single manpack system for an entire global force.

  

THE CON’S CENTRAL ADMISSION AND NTC FINDINGS 8 AND 10

The CoN’s opening problem statement — that the Army “lacks the ability to sense, locate, attack, and protect in and through the EMS across competition and geographic continuums” — maps directly onto Findings 8 and 10. Three decades of post-Cold War divestiture left the service with a fragmented portfolio of EW capabilities distributed across warfighting functions without coherent architecture or unified command. The result, documented at Fort Irwin and confirmed in Ukraine, is an inability to fight as an integrated electromagnetic entity.

The CoN goes further, acknowledging that “EMSO capabilities are spread across different warfighting functions and not fully designed as cohesive technologies that are modular, scalable, and adaptable enough to mitigate modern threats.” For NTC rotation observers, this language will be familiar. It describes exactly the condition that allows the OPFOR to exploit frequency bands the Blue Force’s EW systems cannot jointly manage or defend — because no single element can see or control the full picture.

Joseph Welch, the Program Acquisition Executive for C2/Counter C2, has connected this fragmentation explicitly to procurement philosophy. Speaking at the Army Technical Exchange Meeting prior to the RFI’s release, he described a “product centric inventory” approach — evaluating what high-maturity commercial technologies exist and building toward a capability portfolio rather than procuring individual systems to predetermined specifications. This represents a direct institutional response to Finding 10: if the old procurement model produced one program-of-record jammer after twenty years of effort, a fundamentally different model is required.

 

“The first time a soldier is exposed to being jammed should not be when they’re being graded in their final exercise, or worse yet, in a real-world situation.”
— Maj. Gen. Paul Stanton, Commander, Army Cyber Center of Excellence

 

THE AI/ML BLOCKAGE AND NTC FINDINGS 1, 2, AND 8

The CoN’s most precise structural diagnosis is its explanation of why fragmentation matters operationally: “This prevents the Army from truly leveraging Artificial Intelligence and Machine Learning (AI/ML) for quick decision making to exploit opportunities across the competition continuum.” This statement directly addresses Findings 1, 2, and 8 in combination.

At Fort Irwin, the sequence of failure is predictable and repeatable. The OPFOR jams the Blue Force’s primary network. The common operating picture degrades or disappears. Decision cycles slow as commanders wait for connectivity to restore or attempt to reconstruct the tactical picture via voice. Units act on incomplete information, commit piecemeal, and are defeated in detail. The OPFOR does not need to destroy the visiting unit’s communications entirely — slowing the Blue Force’s decision cycle relative to its own is sufficient.

The AI solution the CoN envisions operates at the machine-speed layer beneath human decision cycles. Col. Scott Shaffer, PM EW&C, expressed this precisely: “Unlocking the potential for spectrum operations at machine speed will be key to winning the EMS fight.” An AI-enabled EMSO architecture can, in principle, detect jamming, characterize the threat, assess its electromagnetic impact on friendly systems, identify countermeasures, and execute — all faster than a human operator can reach for a radio handset. But this architecture requires exactly the coherent, modular, software-defined “Common Services” baseline the CoN specifies in its fourth functional category. Without a common software layer sharing data across EW, signals intelligence, and communications systems, there is no substrate for AI to operate on.

Russia has already fielded this capability in limited form. The RB-109A Bylina system, documented by CSIS in 2024, employs AI-driven algorithms to autonomously coordinate jamming across multiple frequency bands without manual operator input. DARPA’s PROWESS program — developing processors that reconfigure themselves within 50 nanoseconds — represents the U.S. aspiration to match this. The gap between aspiration and fielded capability is precisely the space the CoN is trying to close.

MACHINE SPEED, PACE PLANS, AND NTC FINDINGS 2 AND 3

NTC Finding 3 — the inadequacy of PACE plan training and contingency/emergency tier capabilities — surfaces a subtler dimension of the CoN’s AI/machine-speed imperative. The PACE framework (Primary, Alternate, Contingency, Emergency) was designed for a world where communications degradation is an exceptional condition requiring deliberate human action to manage. Ukraine has demonstrated that in a peer EW environment, degradation is the baseline condition — not the exception.

In the Donbas, Russian forces adapted their jamming techniques so rapidly — compressing the countermeasure lag from more than a month in early 2023 to as little as two or three days by late 2024 — that Ukrainian units could not rely on any given primary system remaining viable for the duration of a single operation. The PACE framework, as implemented in most Army units, requires human recognition of degradation, human decision to transition, and human execution of the fallback — a process that consumes minutes or tens of minutes in a training environment and considerably longer under combat stress.

A machine-speed EMSO architecture changes this. An AI-enabled spectrum management system, such as the Spectrum Situational Awareness System (S2AS, awarded to 3dB Labs in 2025) combined with the Electronic Warfare Planning and Management Tool (EWPMT-X), can detect degradation in milliseconds, assess which PACE tier remains viable, and autonomously route traffic to that tier while alerting commanders — reducing the cognitive burden on the operator from the decision itself to the verification of a machine recommendation. The S2AS’s primary function — providing real-time visualization of a unit’s own electromagnetic signature and interference environment — is in essence a machine-speed answer to the PACE problem: it tells you which of your communication paths are currently viable before you need them.

 

“Until you’ve been jammed, you don’t know that you’re being jammed. We have to let commanders know what they look like in their own backyard.”
— Maj. Gen. Paul Stanton, Army Cyber Center of Excellence, 2024

 

THE TRAINING DEFICIT AND NTC FINDINGS 4, 5, AND 9

NTC Finding 5 — the “first-jam-at-Fort-Irwin problem” — is structural and regulatory, not merely doctrinal. Maj. Gen. Paul Stanton was direct about it: “We are exposing our forces to the EMS at the training center. That’s not good enough.” The Army owns large portions of the electromagnetic spectrum at Fort Irwin for training purposes, but the same capability does not exist at home-station installations, where activating jammers requires FAA coordination and creates interference risks for commercial aviation and telecommunications. The Joint Staff manual on EW training identifies only White Sands Missile Range and the Nevada Test and Training Range as sites where cellular and GPS jamming exercises occur with any regularity.

The Army Special Warfare Center and School has formally requested expanded spectrum testing ranges to address this. The Transforming-in-Contact initiative’s injection of the MEMSS system into units as a prototype capability is an attempt to create recurring exposure at home station before units reach Fort Irwin. But the regulatory environment has not changed, and the gap persists.

The CoN’s emphasis on AI and edge computing is relevant here in a counterintuitive way. A sufficiently autonomous EMSO system reduces the demand on individual operator knowledge — the system characterizes the threat and proposes countermeasures rather than requiring the soldier to do so from memory. This partially addresses Finding 9 (expertise too thin and too senior) by distributing embedded capability rather than requiring distributed expertise. It does not eliminate the need for trained EW specialists, but it lowers the floor on what a non-specialist commander needs to know to function in a jammed environment.

Finding 4’s doctrinal-cultural gap — the disconnect between combat arms officers and EW specialists — requires a different fix. The Army’s Cyber School partnership with the Maneuver Center of Excellence to embed jamming exposure in infantry and armor professional military education is the right approach. The EW Board of Directors, led at the three-star level under the G-3/5/7, ensures that this cultural change has senior institutional backing. But as Capt. Thomas at the NTC acknowledged, bridging the gap between technical EW knowledge and tactical understanding is “a steep learning curve that can boggle my mind even now” — suggesting the solution is years in the making rather than immediately achievable.

THE DRONE RF VULNERABILITY AND NTC FINDING 7

NTC Finding 7 — that drone operators’ RF control frequencies can be intercepted to harvest video feeds and geolocate operators — appears at first glance to be outside the CoN’s EMSO scope. It is not. The CoN’s “Protect” function category explicitly includes emissions control and obfuscation as requirements. The MEMSS program’s classified “radio frequency technical effects” and the CSR (Covert Spectrum Reconnaissance) program’s low-probability-of-detection/low-probability-of-attribution non-kinetic effects are both aimed at the underlying problem: reducing the Army’s exploitable electromagnetic signature.

In Ukraine, Russia’s exploitation of drone RF signatures has been particularly consistent. Russian forces tune to the control frequencies of Ukrainian UAS — including commercial platforms like DJI Mavic variants — harvest the video downlink for intelligence, and in many cases use the RF emission to vector artillery or loitering munitions onto the operator’s position. NTC OPFOR has replicated this technique at Fort Irwin, and inexperienced operators have repeatedly been “striked” by what they assumed was an undetectable platform.

The fiber-optic FPV drone — which renders jamming entirely irrelevant — adds a further dimension to the protect function. A wired drone cannot be jammed or geolocated through its control link because there is no RF emission to intercept. Russia is producing approximately 50,000 fiber-optic FPVs monthly as of early 2026. The CoN’s requirement for protect capabilities that include obfuscation and non-kinetic effects must account for threats that cannot be countered by any technique within the electromagnetic spectrum — a boundary condition that the “Common Services” architecture will need to explicitly accommodate.

THE AI DISINFORMATION PROBLEM AND NTC FINDING 6

NTC Finding 6 — the OPFOR’s use of generative AI to spoof commander voice orders, fabricate operational orders, and flood the training environment with deepfake disinformation — is the finding least directly addressed by the EMSO CoN. The CoN’s four functional categories (Attack, Support, Protect, Common Services) operate on the physical and software layers of the electromagnetic spectrum. AI-generated voice spoofing operates on the cognitive and trust layers — it does not jam a frequency, it corrupts the information carried on that frequency.

Yet the connection to EMSO is real and growing. The most effective use of AI voice spoofing in the NTC environment exploits exactly the conditions that EMS degradation creates. When units are jammed off primary digital networks and forced onto voice radio contingency tiers, authentication protocols degrade and the cognitive load on commanders increases. A commander receiving a suspicious order on a congested voice net — while simultaneously managing a jamming event, a drone contact, and degraded situational awareness — is maximally vulnerable to voice spoofing. The attack is enabled by the EMSO failure, not independent of it.

Col. Black’s description of the OPFOR’s AI capability — “anything from AI-generated memes to AI-generated operational orders, AI-generated voice manipulation and spoofing” — describes a threat that Ukraine has documented in operational use by Russian forces, including Leer-3 system operators who have pushed SMS messages and social media content through captured civilian cellular infrastructure to demoralize Ukrainian soldiers and units. The “Protect” function of the CoN, if construed broadly, should encompass electromagnetic protection of the authentication infrastructure that validates command authority — a capability gap the current EWPMT architecture does not close.

THE UKRAINE VALIDATION AND THE PROCUREMENT URGENCY

Fort Irwin’s controlled training environment has been validated — with lethal operational stakes — by the Ukrainian battlefield. Every NTC finding has its Ukrainian counterpart, and in most cases the Ukrainian version is more severe because it occurs against an adversary with decades of EW investment, no safety constraints, and the incentive structure of existential conflict.

The CoN’s statement that “the cat and mouse game of electronic warfare is waged not in weeks or months like the Cold War of yesteryear, but rather days and hours” maps precisely onto NTC Finding 10’s indictment of the acquisition cycle. An Army that took twenty years to field a single program-of-record ground jammer cannot respond to an adversary updating countermeasures every 48 to 72 hours. The agile funding pilot approved in the FY2026 budget — consolidating EW, UAS, and counter-UAS into a single portfolio with flexible reprogramming authority — is the institutional mechanism for compressing that cycle. The CoN’s problem-statement acquisition philosophy is the contracting mechanism. Together, they represent the most significant Army procurement reform since the post-Cold War drawdown.

It is worth noting what Ukraine also revealed about the limits of this approach. RAND Europe’s November 2025 analysis found that nearly all Allied EW equipment produced before 2020 was functionally unusable by the time it reached Ukrainian frontline units — Russian adaptation had rendered it obsolete in transit. Only some equipment from 2021 through 2022 remained viable at delivery. This suggests that even the CoN’s agile acquisition model, if it produces systems in the 2028–2030 timeframe, must be designed from inception for continuous software-driven update — not fielded as finished products but as updateable platforms, closer in philosophy to commercial software development than traditional defense acquisition.

 

“We are definitely seeing, many times over, an over-reliance on technology. The OPFOR doesn’t need to destroy your communications — slowing your decision cycle is enough.”
— Capt. Jake Thomas, Information Warfare Section, 11th Armored Cavalry Regiment OPFOR

 

WHAT THE CON DOES NOT ADDRESS: THE HUMAN FIX

The most persistent lesson from Fort Irwin is also the one most resistant to acquisition solutions: the decisive variable in an electromagnetically contested environment is the human leader who recognizes degradation, transitions the unit’s decision-making to a lower-bandwidth mode, and continues to fight coherently while trusting subordinates to act on decentralized orders. The 11th ACR’s OPFOR demonstrates this is achievable — Col. Black’s regiment uses all four PACE tiers simultaneously and does not slow down when one fails, because its junior leaders are trained and trusted.

The CoN addresses the technological preconditions for this behavior — resilient architectures, AI-enabled situational awareness, modular systems that fail gracefully rather than catastrophically. But it cannot mandate the cultural shift that Stanton, Thomas, and Black all describe as the central requirement. That shift requires sustained doctrinal change, professional military education reform, and a willingness at the institutional level to grade commanders on how they perform when the network fails rather than measuring readiness by how rarely the network fails.

The EW Board of Directors, the March 2025 EW Strategy, the Cyber School–Maneuver Center partnership, and the Transforming-in-Contact injection of EMSO capabilities into operational units are all structural moves toward this culture. The NTC OPFOR’s continued success against rotating units — documented through 2025 — suggests that structural moves are necessary but not yet sufficient.

CONCLUSION: THE CON AS INSTITUTIONAL MIRROR

The Army’s February 2026 EMSO Characteristics of Need document is most usefully read not as an acquisition notice but as an institutional mirror. It reflects back, in the formal language of program management and requirements engineering, exactly what the 11th Armored Cavalry Regiment’s OPFOR has been demonstrating in the Mojave Desert for years: that the Army’s electromagnetic warfare capability is fragmented, culturally underinvested, organizationally underweighted, and structurally incompatible with the speed at which peer adversaries operate and adapt.

The four CoN solution areas — Attack, Support, Protect, Common Services — map directly onto the NTC’s diagnostic findings. The AI/machine-speed requirement addresses the cognitive paralysis and PACE failure findings. The Common Services baseline addresses the fragmentation finding. The agile funding structure addresses the procurement incapacity finding. The Transforming-in-Contact delivery model addresses, partially, the training deficit finding. What it cannot address in a contract vehicle is the cultural and doctrinal transformation that Capt. Thomas, Maj. Gen. Stanton, and Col. Black all describe as the harder and more important problem.

Industry partners responding to the March 13 RFI should understand that the Army is not looking for the next exquisite, purpose-built electronic warfare system. It is looking for building blocks of a coherent architecture that can be fielded rapidly, updated continuously, and operated by units whose EW proficiency will, for the foreseeable future, fall well short of what Fort Irwin keeps exposing as necessary. The best responses will be not only technically resilient but cognitively accessible — systems that help a jammed, degraded, cognitively overloaded commander fight effectively, rather than systems that require a highly trained specialist to employ. That, precisely, is what the OPFOR keeps proving the visiting force lacks.

 

 

 

SOURCES AND CITATIONS

NTC / OPFOR Training Sources

[1] Freedberg, Sydney J. Jr. "‘California Jammin’: Wargames Show Army's Electronic Weakness — and a Human Fix." Breaking Defense, 2025. https://breakingdefense.com/2025/02/california-jammin-wargames-show-armys-electronic-weakness-and-a-human-fix/

[2] Lawrence, Drew F. "Army Trying to Expose Entire Force to Electromagnetic Warfare During Training." DefenseScoop, August 17, 2023. https://defensescoop.com/2023/08/17/army-trying-to-expose-entire-force-to-electromagnetic-warfare-during-training/

[3] Mizokami, Kyle. "Preparing for Electronic Warfare Is the Army’s Top Cyber Priority in 2024." Defense One, March 22, 2024. https://www.defenseone.com/defense-systems/2024/03/preparing-electronic-warfare-armys-top-cyber-priority-2024/395177/

[4] Stanton, Maj. Gen. Paul (quoted). "Army Expanding Electronic Warfare Training for Every Soldier." AFCEA Signal, August 2023. https://www.afcea.org/signal-media/cyber-edge/army-expanding-electronic-warfare-training-every-soldier

[5] Tucker, Patrick. "Special Operators Seek Larger Ranges for Electronic Warfare and Drone Development and Training." Defense One, December 18, 2025. https://www.defenseone.com/technology/2025/12/special-operators-seek-expanded-electronic-drone-warfare-test-sites-us/410248/

[6] Gambone, Lt. Col. Michael and Carey, Abigail. "TRADOC Hosts 27th Annual Worldwide OPFOR Conference." U.S. Army, March 22, 2024. https://www.army.mil/article/274746/tradoc_hosts_27th_annual_worldwide_opfor_conference

[7] QinetiQ. "Rethinking PACE for a More Agile, Threat-Driven World." QinetiQ Blog, 2025. https://www.qinetiq.com/en/blogs/rethinking-pace-for-a-more-agile-threat-driven-world

EMSO CoN / Army Acquisition Sources

[8] Pomerleau, Mark. "Army Issues Broad Appeal to Industry for Electromagnetic Spectrum Solutions." Breaking Defense, February 25, 2026. https://breakingdefense.com/2026/02/army-issues-broad-appeal-to-industry-for-electromagnetic-spectrum-solutions/

[9] Lawrence, Drew F. "Army’s New Budget Proposal Invests in Electromagnetic Force Protection Capabilities." DefenseScoop, July 1, 2025. https://defensescoop.com/2025/07/01/armys-2026-budget-request-electronic-warfare-force-protection-capabilities/

[10] Lawrence, Drew F. "Army Seeks More Flexible Funding on Electronic Warfare Capabilities, Programs." DefenseScoop, October 17, 2024. https://defensescoop.com/2024/10/17/army-seeks-flexible-funding-electronic-warfare-capabilities-programs/

[11] Lawrence, Drew F. "What Does Flexible Funding for Electronic Warfare Mean for the Army?" DefenseScoop, April 18, 2025. https://defensescoop.com/2025/04/18/army-electronic-warfare-flexible-funding/

[12] Lawrence, Drew F. "Army Evaluates Several Evolving Electronic Warfare Concepts at Project Convergence." DefenseScoop, April 15, 2025. https://defensescoop.com/2025/04/15/army-project-convergence-electronic-warfare-concepts/

[13] Lawrence, Drew F. "Electronic Warfare Receiving More Senior Level Attention Within the Army." DefenseScoop, August 21, 2025. https://defensescoop.com/2025/08/21/electronic-warfare-army-senior-level-attention/

[14] Lawrence, Drew F. "Army Wants ‘Self-Organized’ Industry Teams for Next-Gen C2 Effort." DefenseScoop, March 31, 2025. https://defensescoop.com/2025/03/31/army-next-gen-c2-self-organized-industry-teams/

[15] Lawrence, Drew F. "One Electronic Warfare Payload to Rule Them All?" DefenseScoop, August 22, 2025. https://defensescoop.com/2025/08/21/army-electronic-warfare-modular-mission-payload-vision/

[16] U.S. Army PEO IEW&S. "U.S. Army Electromagnetic Warfare Capabilities Update." July 7, 2025. https://peoiews.army.mil/2025/07/07/us-army-electromagnetic-warfare-capabilities-update/

[17] Strobel, Warren P. "House NDAA Provision Would Require Army to Create Electronic Warfare Center of Excellence." DefenseScoop, May 13, 2024. https://defensescoop.com/2024/05/13/house-ndaa-provision-would-require-army-to-create-electronic-warfare-center-of-excellence/

[18] "Here Are the Army’s New Planned EW, Signals Programs." Breaking Defense, December 2023. https://breakingdefense.com/2023/12/here-are-the-armys-new-planned-ew-signals-programs/

Ukraine Battlefield Evidence

[19] TRADOC G-2 OE Enterprise. "Ukrainian Unmanned Aerial System Tactics." https://oe.tradoc.army.mil/product/ukrainian-unmanned-aerial-system-tactics/

[20] "Russia’s Changes in the Conduct of War Based on Lessons from Ukraine." Military Review, September–October 2025. https://www.armyupress.army.mil/Journals/Military-Review/English-Edition-Archives/September-October-2025/Lessons-from-Ukraine/

[21] CLAWS. "Russia-Ukraine War: Lessons from an Electronic Warfare (EW) Perspective." May 31, 2025. https://claws.co.in/russia-ukraine-war-lessons-from-an-electronic-warfare-ew-perspective/

[22] Defense.info. "Russian Learning from Ukrainian Drone Warfare: A Strategic Adaptation Analysis." June 24, 2025. https://defense.info/re-shaping-defense-security/2025/06/russian-learning-from-ukrainian-drone-warfare-a-strategic-adaptation-analysis/

[23] "How Ukraine’s Drone War Is Forcing the U.S. Army to Rewrite Its Battle Doctrine." Military.com, October 19, 2025. https://www.military.com/feature/2025/10/19/how-ukraines-drone-war-forcing-us-army-rewrite-its-battle-doctrine.html

[24] "Adapting the Combat Training Centers for the Drone Battlefield." Small Wars Journal, January 8, 2026. https://smallwarsjournal.com/2026/01/08/adapting-the-combat-training-centers-for-the-drone-battlefield/

[25] RAND Europe. NATO EW Coalition and Ukraine EW assessment, November 2025 (cited in multiple sources).

AI, Disinformation, and Deepfakes

[26] Doolittle, Claudia. "ROTC Students Are Helping the Military Defend Against AI Deepfakes." Military Times, December 31, 2025. https://www.militarytimes.com/news/your-military/2025/12/31/rotc-students-are-helping-the-military-defend-against-ai-deepfakes/

[27] Vossler, Joseph, et al. "Artificial Intelligence as a Force Multiplier in U.S. Military Information Campaigns." CSIAC, October 28, 2024. https://csiac.dtic.mil/articles/artificial-intelligence-as-a-force-multiplier-in-u-s-military-information-campaigns/

[28] Brawner, Keith, Ph.D. "Preparing for the Cyber Battlespace." Army AL&T Magazine, August 25, 2025. https://www.army.mil/article/287582/preparing_for_the_cyber_battlespace

Strategic Context

[29] Clark, Bryan et al. "The Invisible Battlefield: A Technology Strategy for US Electromagnetic Spectrum Superiority." Hudson Institute. https://www.hudson.org/national-security-defense/the-invisible-battlefield-a-technology-strategy-for-us-electromagnetic-spectrum-superiority

[30] "Joint Electronic Warfare, Cyber and Spectrum Operations Need Work to Face Contested Environments." AFCEA Signal Magazine. https://www.afcea.org/signal-media/defense-operations/joint-electronic-warfare-cyber-and-spectrum-operations-need-work

[31] U.S. Army Communications-Electronics Command. "Army Software & Innovation Center Enables Army Continuous Transformation." Army.mil, January 20, 2026. https://www.army.mil/article/289983/army_software_innovation_center_enables_army_continuous_transformation

[32] Carpenter, Brig. Gen. Steve (quoted). "Army’s Training Center in Europe Modernizing and Optimizing Training for Global Deterrence." Army.mil, April 2, 2025. https://www.army.mil/article/284256/armys_training_center_in_europe_modernizing_and_optimizing_training_for_global_deterrence

[33] Joseph D. Welch Biography. U.S. Army, May 2024. https://api.army.mil/e2/c/downloads/2024/05/06/13983eea/joseph-welch-bio-1.pdf

[34] Capaccio, Anthony, and Taylor, Col. Shane. "Army Network Plan Will Offset Contested Comms with Multi-Path Transport-Agnostic Capabilities." Breaking Defense, December 2022. https://breakingdefense.com/2022/12/army-network-plan-will-offset-contested-comms-with-multi-path-transport-agnostic-capabilities/

[35] Lawrence, Drew F. "Army Expects to Mature Electromagnetic Spectrum Decoy and Obfuscation Systems in FY ’25." DefenseScoop, March 22, 2024. https://defensescoop.com/2024/03/22/army-electromagnetic-spectrum-decoy-obfuscation-systems-2025/

 

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