Japan to put hypersonic missile-tracking technology to the test in space | South China Morning Post

Launch of the H3 Launch Vehicle with HTV-X Cargo Spacecraft

Japan to put hypersonic missile-tracking technology to the test in space | South China Morning Post

Japan Deploys Orbital Hypersonic Tracking Experiment as Regional Threat Detection Race Intensifies

Ministry of Defence infrared payload aboard HTV-X cargo craft marks Tokyo's entry into contested space-based missile warning domain

TOKYO — Japan's Ministry of Defence has quietly deployed experimental infrared tracking sensors aboard the newly arrived HTV-X cargo spacecraft in what officials describe as a critical pathfinder for indigenous space-based hypersonic missile detection capabilities, adding a new player to an increasingly competitive orbital surveillance architecture.

The HTV-X, launched Oct. 26 from Tanegashima Space Center aboard an H3 rocket and captured by the International Space Station's robotic arm Oct. 30, carries more than the 4.4 metric tons of routine logistics supplies. Embedded within the mission are Ministry of Defence infrared sensors designed to detect and track hypersonic glide vehicles exceeding Mach 5—a capability that has proven elusive even for established space powers.

The ¥3.8 billion ($24.7 million) experiment represents Japan's first operational foray into space-based missile tracking, coming as China's DF-17, Russia's Avangard, and North Korea's Hwasong-series hypersonic systems proliferate across the Indo-Pacific. Ministry officials confirmed the spacecraft will conduct tracking trials against simulated hypersonic targets during its extended 18-month post-ISS free-flight phase, with successful demonstration potentially leading to dedicated satellite deployment.

The Hypersonic Tracking Problem

The fundamental challenge in hypersonic tracking has less to do with detection physics than with maintaining custody of highly maneuverable targets.

When hypersonic vehicles exceed Mach 5, atmospheric friction raises nose cone and leading edge temperatures to 3,000-5,000°F, generating intense infrared signatures. However, distinguishing these signatures against Earth's thermal background has been compared to identifying a slightly brighter candle in a sea of candles.

Radar tracking presents frequency-dependent challenges. Lower-frequency systems—HF, UHF, and S-band—suffer significant attenuation from the plasma sheath enveloping hypersonic vehicles. X-band (8-12 GHz) and higher-frequency radars including Ku-band, Ka-band, and millimeter-wave systems penetrate the plasma more effectively, but face a different constraint: update rate.

Traditional ballistic missiles follow predictable parabolic trajectories amenable to Kalman filtering with relatively sparse radar updates. Hypersonic glide vehicles execute sustained aerodynamic maneuvers throughout flight, demanding dramatically higher update rates to maintain track custody. Ground-based radars additionally face line-of-sight limitations against low-altitude, maneuvering targets—the classic "radar horizon" problem that becomes acute when seconds matter.

This has driven the architecture toward persistent infrared surveillance from space, where continuous thermal tracking compensates for unpredictable kinematics.

U.S. SBIRS: The Baseline Capability

The U.S. Space-Based Infrared System, operational since 2011, established the current state-of-practice for orbital missile warning. Operating from geosynchronous orbit at 22,000 miles altitude, SBIRS provides continuous hemispheric surveillance, scanning Earth's surface every 10 seconds for infrared activity. The system detects missile launches faster than any alternative while determining missile type, burnout velocity, trajectory, and probable impact point.

SBIRS satellites carry two distinct infrared sensors: a scanning sensor providing 24/7 global coverage, and a step-staring sensor with precision pointing for theater missions. Operating in short-wave and mid-wave infrared bands, the sensors detect any significant thermal event globally.

However, SBIRS was optimized for traditional ballistic missiles. China and Russia are fielding hypersonic, low-flying missiles that produce infrared signatures 10-20 times dimmer than ballistic threats—signatures the current architecture struggles to maintain custody of beyond boost phase.

The geometry compounds the problem. GEO-based satellites provide persistent stare coverage for boost-phase detection while monitoring theater ballistic missile events, but were never designed to continuously track maneuvering, very-low-altitude hypersonic warheads after booster separation.

The LEO Proliferation Strategy

Recognizing GEO's limitations, the U.S. Space Development Agency has pivoted to proliferated low Earth orbit constellations operating below 2,000 km altitude.

In lower orbits, sensors detect dimmer signatures than higher-altitude systems can resolve. Crucially, LEO systems maintain visibility of hypersonic glide vehicles as they maneuver and generate atmospheric friction heating.

The architecture exploits geometric diversity: multiple satellites detecting a given engagement provide different look angles enabling accurate three-dimensional tracking. Twenty-eight satellites launched in 2024-2025 form the initial tracking kernel.

SDA intends to field at least 1,000 satellites in LEO by 2026 as part of its Proliferated Warfighter Space Architecture. The Space Force requested nearly $16 billion over five years for these capabilities, with L3Harris Technologies, Northrop Grumman, Raytheon Technologies, and SpaceX contracted for the Tracking Layer.

The proliferated approach fundamentally changes the defensive economics. With hundreds of satellites distributing the surveillance function, the cost to attack the constellation exceeds the replacement cost—the defensive bullet now costs more than the satellite.

Sensor Fusion Architecture

Effective hypersonic defense requires comprehensive sensor fusion combining space-based radar and infrared optical sensors. Infrared excels at target acquisition; space-based radar provides trajectory tracking. High-speed processing enables continuous fusion—when radar loses track, infrared assists reacquisition.

Multispectral electro-optical/infrared sensor suites spanning ultraviolet to long-wavelength infrared, with extremely low background noise performance, serve as optimal surveillance augmentation for hypersonic missile defense detection and tracking.

Space-based infrared networks easily cover Earth's entire surface, making them ideal for early warning. Current sensor technology, however, lacks the precision required for interception fire control, positioning these systems as complements to radar rather than replacements.

L3Harris Technologies has advanced multiple orbital layers for the integrated architecture. The company completed preliminary design review for infrared payloads destined for the Missile Track Custody constellation in medium Earth orbit and successfully launched five tracking satellites in February 2024 for the Hypersonic and Ballistic Tracking Space Sensor program.

Next-Gen OPIR: The GEO Evolution

The Space Force's Next-Generation Overhead Persistent Infrared program represents the planned SBIRS succession, with first GEO launches now scheduled for March 2026. Next-Gen OPIR sensors deliver three times the sensitivity and twice the accuracy of SBIRS, specifically designed to detect faster-burning, dimmer missile boost technologies.

Operating from 22,000-mile GEO vantage points, Next-Gen OPIR satellites maintain unmatched mid-latitude coverage for ballistic missiles, hypersonics, and emerging threats. The satellites employ Lockheed Martin's LM 2100 combat bus with enhanced resiliency features addressing counter-space threats.

Next-Gen OPIR provides downlink data rates four times greater than SBIRS. Data collected during initial attack phases cues sensors in other orbital layers for continuous tracking through post-boost and mid-course flight.

The architecture aims for multiple fields of view over designated regions without deploying massive GEO constellations. Next-Gen OPIR works in tandem with SBIRS, future Next-Gen OPIR Polar satellites, and SDA's LEO tracking layer in a seamless, resilient, multi-layered national missile warning and defense architecture.

HTV-X: Japan's Experimental Platform

The HTV-X represents more than incremental cargo vehicle improvement. After completing its three-month ISS berthing, the spacecraft operates as an on-orbit experimental platform for up to 18 months—extended mission capability distinguishing it from the predecessor HTV, which typically remained ISS-attached for only one month.

Performance improvements include cargo loading time reduced from 80 to 24 hours, power generation increased five-fold to 1 kilowatt via two solar arrays, and payload capacity reaching 5,820 kg using International Standard Payload Racks.

Mitsubishi Heavy Industries built the spacecraft with several technology demonstrations beyond the defense sensors: H-SSOD small satellite deployment mechanism, Mt. FUJI laser-based attitude measurement experiments, DELIGHT deployable lightweight planar antenna demonstrations, and SDX next-generation solar cell tests.

Japanese media reports indicate a projectile simulating a hypersonic missile will be launched during the experimental phase to evaluate the infrared sensors' tracking performance from orbit. Successful demonstration would validate the sensor suite for integration into dedicated surveillance satellites, providing Japan with persistent early-warning independent of ally-provided data.

Regional Threat Proliferation

Hypersonic weapons travel at approximately 3,900 mph—twice bullet muzzle velocity—while executing mid-flight maneuvers that defeat traditional missile defense architectures. Former Missile Defense Agency officials note these weapons fly at lower altitudes than ballistic missiles, exploiting line-of-sight limitations of ground-based radars.

China's DF-17 achieved initial operating capability by 2019, providing the People's Liberation Army Rocket Force with a medium-range hypersonic strike option. Russia's Avangard system entered service in 2019, carried by UR-100N ICBMs. North Korea conducted test launches of the Hwasong-8 in September 2021 and Hwasong-16B in January 2022, though the reliability and operational status of these systems remains subject to debate among Western intelligence analysts.

Japan's Acquisition, Technology & Logistics Agency has pursued the Hyper-Velocity Gliding Projectile program since 2018, focusing on "island defense" scenarios where rapid response to amphibious or expeditionary threats requires standoff strike capability. The HVGP aims for deployment in the late 2020s.

The broader context includes Japan's 2022 National Security Strategy revision, which authorized counter-strike capabilities for the first time since World War II. Space-based persistent tracking enables responsive strike operations by providing continuous target updates—the kill chain closes only with maintained custody.

Technical Challenges Ahead

Hypersonic weapons shift the offense-defense balance toward offense dominance in near-peer competition, with significant implications for crisis stability. The combination of speed, maneuverability, and flight altitude compounds defensive challenges.

Early missile detection proves particularly critical for hypersonic glide vehicles, where speed combined with low-altitude flight makes the radar horizon problem acute. Space-based infrared sensors offer persistent surveillance over wide areas, detecting launch thermal signatures and tracking weapons throughout flight, buying critical additional minutes for defensive response.

Japan's experiment will inform not only indigenous satellite development but contribute valuable data on optimal infrared wavelengths, signal processing algorithms, and sensor sensitivity requirements for hypersonic tracking from LEO. The Ministry of Defence has not disclosed specific technical parameters, though industry sources suggest the sensors operate in multiple infrared bands with enhanced signal-to-noise ratios compared to commercial Earth observation systems.

Whether Japan proceeds with a dedicated constellation or contributes sensors to a multilateral tracking network remains unclear. Ministry officials have discussed potential cooperation with the United States on integrated early warning, while maintaining sovereign capability as a hedge against alliance friction.

The Orbital Economics

The U.S. military is constructing a robust constellation across GEO, HEO, MEO, and LEO. This multilayer architecture rapidly detects, accurately tracks, and precisely targets threats—particularly hypersonic vehicles that elude traditional defenses.

Average satellite costs vary dramatically by orbit. SDA's LEO tracking satellites average $14-15 million per spacecraft, while Next-Gen OPIR GEO satellites approach $2 billion including development amortization. The proliferated LEO approach sacrifices individual sensor capability for aggregate coverage and survivability.

Japan's decision point centers on whether to pursue a small number of exquisite GEO sensors providing theater coverage, or contribute to a proliferated LEO architecture with international partners. Budget realities favor the latter—Japan's space program operates under fiscal constraints that make multi-billion-dollar flagship programs increasingly difficult to justify.

The HTV-X experiment provides empirical data informing this choice. Successful tracking demonstration from LEO would validate the technical approach while revealing operational limitations. Conversely, poor signal-to-noise ratios or inadequate custody maintenance would argue for higher-orbit systems despite their greater vulnerability.

Looking Forward

As HTV-X begins its extended experimental mission, the next 18 months will prove whether Japan's approach to hypersonic threat detection provides actionable early warning in operationally relevant scenarios. The spacecraft's successful deployment contributes to international understanding of space-based missile tracking while advancing Japan's indigenous capabilities.

The experiment occurs against accelerating regional arms competition. China's defense industrial base produces hypersonic systems at scale, while Russia continues development despite Ukraine war distractions. North Korea's programs, while less mature, demonstrate sustained commitment to asymmetric capabilities.

Japan's investment signals determination to maintain technological parity in an era defined by hypersonic weapons and space-based surveillance. Whether operating as part of a multilateral detection network or as an independent capability, the HTV-X mission represents Tokyo's recognition that future security depends on eyes overhead—and the ability to maintain custody when seconds determine outcomes.

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Software-Driven Radar Upgrade Doubles Detection Range

Ukraine’s latest radar developments turning the tide in the skies - YouTube

Drone Warfare Dominates Ukraine Battlefield

Dutch firm Robin Radar Systems extends IRIS counter-drone detection from 5 to 12 kilometers through AI-powered software, addressing threat that now causes up to 80% of combat casualties

By Stephen L Pendergast with Claude Anthropic
October 30, 2025

As unmanned aerial systems fundamentally reshape modern warfare, a critical technology gap is closing. Robin Radar Systems has unveiled a software-only upgrade to its battlefield-proven IRIS counter-drone radar that more than doubles its detection range—from 5 kilometers to 12 kilometers—providing defenders with precious additional seconds to respond to incoming threats.

The upgrade arrives at a pivotal moment. Attack drones now account for 70 to 80 percent of all battlefield casualties in the Russia-Ukraine war, according to Western officials, marking a fundamental shift from traditional artillery-based combat. Ukraine currently faces at least 100 one-way attack drones every day—approximately one every 15 minutes.

Battlefield-Tested Innovation

The IRIS Long-Range Mode was developed and stress-tested through extensive deployments in Ukraine, shaped by direct feedback from Ukrainian operators, said Robin Radar Systems CEO Siete Hamminga. The system now provides critical early warning against fast-moving Shahed loitering munitions and other fixed-wing drones.

"This upgrade isn't just about range—it's about time," said Kristian Brost, General Manager of Robin Radar USA. "Every extra kilometer of detection gives defenders more precious seconds to react, more chances to protect critical infrastructure, and ultimately, more lives saved."

The 29-kilogram system delivers 360-degree azimuth coverage and can operate while mounted on moving vehicles traveling up to 100 kilometers per hour. The radar requires no hardware replacement, allowing all existing IRIS units to be upgraded in the field.

The Evolving Shahed Threat

The Shahed drone, Iran's most widely exported military technology, has become synonymous with Russia's aerial assault campaign. The Iranian-designed Shahed-136, produced by Russia as the Geran-2, weighs approximately 200 kilograms and travels at speeds exceeding 180 kilometers per hour. These systems can cover approximately 2,500 kilometers and are priced up to $80,000 each.

Russia has continuously modified the design to make detection more difficult. New jet-powered variants now travel between 380 and 400 kilometers per hour, with recorded speeds reaching 477 kilometers per hour. The jet-powered Geran-3 variant can reach altitudes of 9,000 meters and carries warheads weighing between 50 and 300 kilograms.

Russia now produces more than 5,000 long-range drones each month—approximately 2,700 Geran strike drones and 2,500 decoy models, marking nearly a fivefold increase since summer 2024.

AI-Powered Precision

The IRIS radar's effectiveness stems from sophisticated micro-Doppler technology and deep neural networks trained on battlefield data. The system focuses on the unique micro-Doppler signatures emitted by drones during flight, building a database that distinguishes between birds, drones, and other airborne objects instantaneously.

The deep neural network analyzes behavioral characteristics such as speed, flight patterns, and size, enabling precise classification of aerial targets. This AI integration has achieved what the manufacturer describes as "a very low false positive rate," ensuring operators receive alerts only for genuine threats rather than wildlife.

The system underwent several upgrades driven by Ukrainian Armed Forces' demand for mobile counter-drone capabilities, including enhancements to track fast first-person-view (FPV) drones that conventional radars struggled to detect.

Strategic Deployment

More than 200 Robin Radar systems are now deployed in Ukraine, protecting critical infrastructure, supporting frontline operations, and adapting to evolving drone threats. The Dutch Ministry of Defence purchased 51 IRIS radars in 2023, ordered another 51 in 2024 with on-the-move capability, and doubled that order in 2025—all for donation to Ukraine.

The radar costs less than $1 million per unit, according to Hamminga—significantly below the €500,000 starting price cited in earlier marketing materials. This positions IRIS as a comparatively affordable option in the counter-drone market, though still a substantial investment for many potential users.

The battlefield-tested technology is being rolled out to European Ministries of Defence and Interior, as well as the U.S. Department of Homeland Security.

Broader Drone Warfare Impact

The dominance of drones extends beyond traditional combat casualties. Short-range drones killed at least 395 civilians and injured 2,635 between February 2022 and April 2025, according to the UN Human Rights Monitoring Mission. In January 2025 alone, short-range drones caused more casualties than any other weapon in Ukraine, accounting for 27 percent of civilian deaths and 30 percent of injuries.

Ukraine claims to have manufactured 2.2 million drones in 2024 and aims to produce 4.5 million in 2025. Russia produces approximately 300 long-range drones daily, compared to Ukraine's 100.

FPV drones have become a central pillar of Ukraine's war effort, inflicting up to 80 percent of Russian battlefield casualties and enabling Ukrainian forces to hold the line despite artillery shell shortages. By early 2025, Ukraine was producing 200,000 FPV drones per month.

Expanding Counter-Drone Market

The surge in drone warfare is driving explosive growth in counter-drone technologies globally. The global anti-drone market was valued at $2.45 billion in 2024 and is projected to reach $10.58 billion by 2030, growing at a compound annual growth rate of 27.2 percent.

Anti-drone radar technology dominated the counter-drone technology segment in 2024, owing to advanced detection features, long-range capabilities, and high accuracy in differentiating between drones and other aerial objects. The counter-drone radar market specifically stood at $1.2 billion in 2024 and is forecast to reach $5.7 billion by 2033.

Robin Radar originated as a bird-spotting technology company in the early 2010s. The company turned to drones as predictable targets to validate its bird-detection radars, a practical decision that proved fortuitous. Drone detection now accounts for the majority of the company's revenue.

Private equity firm Parcom became a majority owner in 2024, positioning the company for growth. Robin Radar expects to end 2025 with around 225 employees, up from approximately 25 in 2020.

Defense Integration and Future Challenges

The IRIS radar typically operates as part of layered defense systems rather than as a standalone solution. Detection systems like IRIS provide situational awareness that enables timely deployment of countermeasures—including electronic warfare systems for signal jamming, kinetic interceptors, and directed energy weapons.

As drone technology advances with faster speeds, stealth features, and autonomous capabilities, the counter-drone industry faces an ongoing arms race. Russia often flies Shaheds at altitudes as low as 100 meters above ground, giving defenders only five or six seconds to find, target, and destroy them.

The jet-powered Geran-3's higher flight ceiling and dive speed reduce the effectiveness of autocannon and machine gun-based defenses, forcing defenders to rely on more expensive surface-to-air missiles like NASAMS or Buk. However, the turbojet engine increases the drone's thermal signature, exposing it to infrared-guided interception.

"As software continues to transform defence, it's our every intention that Robin Radar will hold its position at the forefront of innovation," Hamminga said.

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Sources and Citations

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4. EDR Magazine. "DSEI 2025 - Robin Radar Systems deploys the LRM module for IRIS radar." Paolo Valpolini. September 20, 2025. https://www.edrmagazine.eu/dsei-2025-robin-radar-systems-deploys-the-lrm-module-for-iris-radar

5. Defense News. "Dutch radar firm Robin turns bird-spotting skills into drone defense." Rudy Ruitenberg. September 24, 2025. https://www.defensenews.com/global/europe/2025/09/24/dutch-radar-firm-robin-turns-bird-spotting-skills-into-drone-defense/

6. Defence Industry EU. "Robin Radar Systems unveils software upgrade extending IRIS counter-drone radar range to 12km." September 19, 2025. https://defence-industry.eu/robin-radar-systems-unveils-software-upgrade-extending-iris-counter-drone-radar-range-to-12km/

7. Euromaidan Press. "Military wanted to see farther — now Dutch company delivers that with AI-driven Iris radar in Ukraine." October 29, 2025. https://euromaidanpress.com/2025/10/29/military-wanted-to-see-farther-now-dutch-company-delivers-that-with-ai-driven-iris-radar-in-ukraine/

8. Army Technology. "Drones now account for 80% of casualties in Ukraine-Russia war." April 8, 2025. https://www.army-technology.com/news/drones-now-account-for-80-of-casualties-in-ukraine-russia-war/

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11. UN Human Rights Monitoring Mission in Ukraine. "In Ukraine Short Range Drones Become Most Dangerous Weapon for Civilians UN Human Rights Monitors Say." February 11, 2025. https://ukraine.ohchr.org/en/In-Ukraine-Short-Range-Drones-Become-Most-Dangerous-Weapon-for-Civilians-UN-Human-Rights-Monitors-Say

12. The Week. "Death from above: Drones upend rules of war in Ukraine." July 14, 2025. https://theweek.com/politics/death-drones-upend-rules-war-ukraine

13. Atlantic Council. "Drone superpower: Ukrainian wartime innovation offers lessons for NATO." May 13, 2025. https://www.atlanticcouncil.org/blogs/ukrainealert/drone-superpower-ukrainian-wartime-innovation-offers-lessons-for-nato/

14. Kyiv Independent. "How Russia modified Iran's Shahed-136 drones — and what it means for Ukraine." July 17, 2025. https://kyivindependent.com/how-russia-modified-irans-shahed-136-drones-and-what-it-means-for-ukraine/

15. Kyiv Independent. "Explainer: Iran's cheap, effective Shahed drones and how Russia uses them in Ukraine." January 15, 2025. https://kyivindependent.com/explainer-irans-cheap-effective-shahed-drones-and-how-russia-uses-them-in-ukraine/

16. Kyiv Independent. "How Russia's Shahed drones are getting more deadly — and what Ukraine is doing about it." May 20, 2025. https://kyivindependent.com/how-russias-shaheds-are-evolving-and-what-ukraines-air-defenses-are-doing-about-it/

17. Army Recognition. "Drone wreckage confirms that Russian forces employ Iranian Shahed-238 jet-powered drones against Ukraine." 2025. https://www.armyrecognition.com/focus-analysis-conflicts/army/conflicts-in-the-world/russia-ukraine-war-2022/drone-wreckage-confirms-that-russian-forces-employ-iranian-shahed-238-jet-powered-drones-against-ukraine

18. The War Zone. "Russia's Jet Powered Shahed Kamikaze Drone Is A Big Problem For Ukraine." July 31, 2025. https://www.twz.com/news-features/russias-jet-powered-shahed-kamikaze-drone-is-a-big-problem-for-ukraine

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New Technique Cleans Up Synthetic Aperture Radar Images

RFI Removal From SAR Imagery via Sparse Parametric Estimation of LFM Interferences | IEEE Journals & Magazine | IEEE Xplore

Progressive Removal of Multiple Interference Components from SAR Imagery

This figure demonstrates the step-by-step process of removing radio frequency interference (RFI) from a Sentinel-1 synthetic aperture radar image of northern Sweden near the Baltic Sea coast.

Panel Breakdown:

Panel (a) - Original Corrupted Image: Shows the raw SAR image with severe RFI contamination. The bright vertical and diagonal streaks are interference artifacts that completely obscure the underlying terrain features.

Panel (b) - Estimated FM Rate Parameters: A plot displaying the estimated azimuth and range frequency modulation (FM) rates for the detected LFM interference components. The blue triangular markers indicate the locations of three distinct interference components in the parameter space.

Panel (c) - First LFM Component: Shows the most dominant interference component isolated by the algorithm. This represents the strongest interfering signal that needs to be removed.

Panel (d) - After First Removal: The cleaned image after removing the most significant LFM component. Notice that while some interference has been eliminated, substantial artifacts remain, revealing the multi-component nature of the RFI.

Panel (e) - Second LFM Component: The second most significant interference pattern isolated from the partially cleaned image.

Panel (f) - After Second Removal: Further improvement in image quality after removing the second interference component. More of the underlying terrain structure becomes visible.

Panel (g) - Third LFM Component: The third interference pattern detected and isolated, showing the weakest but still significant interfering signal.

Panel (h) - Final Cleaned Image: The final result after iteratively removing all three LFM components. The terrain features are now clearly visible, with the bright vertical strips largely eliminated. The underlying scene structure—including what appears to be coastline and land features—is now interpretable.

Key Insights:

This figure illustrates the paper's central contribution: RFI in SAR imagery often consists of multiple overlapping interference components, not just a single source. Previous methods that assumed single-component interference would have stopped after panel (d), leaving substantial artifacts. The iterative sparse parametric estimation approach successfully identifies and removes each component sequentially, dramatically improving image quality and making the data suitable for scientific analysis and operational use.

 

Detecting Multiple Interference Patterns

Advanced sparse recovery algorithm removes radio frequency interference from satellite radar imagery, improving clarity for Earth observation missions

Synthetic aperture radar (SAR) satellites capture detailed images of Earth's surface day and night, through clouds and darkness. But these systems face a persistent problem: radio frequency interference (RFI) from ground-based radars and other SAR satellites creates bright streaks and artifacts that obscure the underlying terrain. Now, researchers have developed a more sophisticated approach to identify and remove these interference patterns from radar images.

A team led by Dehui Yang at Xi'an Jiaotong-Liverpool University and colleagues at Nanjing University of Science and Technology has introduced a method that recognizes RFI as a mixture of multiple linear frequency-modulated (LFM) signal components, rather than treating it as a single interference source. Their technique, published in October 2025 in IEEE Geoscience and Remote Sensing Letters, uses sparse mathematical representations to efficiently estimate the characteristics of each interference component before systematically removing them.

"One of the challenges in spaceborne synthetic aperture radar is modeling and mitigating radio frequency interference artifacts in SAR imagery," the researchers wrote. Previous approaches assumed interference consisted of a single LFM signal, limiting their effectiveness when multiple radio sources create overlapping artifacts in the same image.

The Growing Interference Problem

Radio frequency interference has become increasingly problematic for SAR systems as more satellites share crowded spectrum bands. The European Space Agency's Sentinel-1 satellites, which provide free radar imagery for environmental monitoring and disaster response, frequently encounter interference from ground-based radars and other space-based systems. In one documented case, China's GaoFen-3 satellite and Europe's Sentinel-1A experienced mutual interference when their radar signals crossed paths.

The interference appears as distinctive bright strips in SAR images—linear frequency-modulated signals that sweep across a range of frequencies, creating characteristic patterns. When one SAR satellite's transmitted signal is received by another satellite as unwanted interference, it manifests as two-dimensional LFM artifacts that can completely obscure ground features.

A Sparse Solution

The new approach models RFI as multiple overlapping LFM components, each characterized by specific frequency modulation rates in both the azimuth (along-track) and range (across-track) directions. The key innovation lies in using sparse parametric estimation—a technique that assumes most of a pre-defined "dictionary" of possible interference patterns will have zero contribution, with only a few patterns actually present in any given corrupted image.

"In practical scenarios, each RFI artifact in SAR images typically contains only a few LFM components," the researchers explained. By solving an optimization problem that minimizes the number of active interference patterns while matching the observed data, the algorithm efficiently identifies which specific LFM components are present.

The method splits the estimation process into two computationally efficient steps: first estimating the azimuth frequency modulation rate (which remains constant across all interference components), then separately estimating the range frequency modulation rates (which vary for each component). Once these parameters are identified, the algorithm uses spectral analysis to focus each interference component, applies notch filtering to remove it, and repeats the process iteratively for multiple components.

Superior Performance

Testing on Sentinel-1 single-look complex images from various locations—including Sweden's Baltic Sea coast, Iran's central desert, and Yemen's Red Sea coast—demonstrated clear improvements over existing techniques. The researchers compared their method against three representative approaches: principal component analysis (PCA), robust PCA (RPCA), and an earlier two-dimensional spectral analysis method that assumed single-component interference.

Using the average gradient metric to quantitatively assess image quality (with higher values indicating richer structural detail), the new method achieved scores of 24.82 and 6.23 on two test images, compared to 21.16 and 6.21 for the previous best approach. Visual inspection revealed that competing methods either removed too much legitimate image content along with the interference, over-smoothed the results, or left significant residual artifacts.

In simulation experiments where synthetic interference was added to clean reference images at various signal-to-interference ratios, the new technique consistently achieved the lowest recovery errors across all interference levels. The advantage was particularly pronounced when multiple interference sources were present—the scenario the method was specifically designed to address.

Implications for Earth Observation

The ability to more effectively remove radio frequency interference has practical implications for numerous SAR applications. These radar systems support disaster response by imaging flood-affected areas through cloud cover, monitor deforestation in remote regions, track ice sheet changes, detect ground subsidence, and provide intelligence for both civilian and defense purposes.

As more nations launch SAR satellites and the orbital environment becomes increasingly congested, interference mitigation will become even more critical. The European Space Agency operates two Sentinel-1 satellites as part of its Copernicus program, with Sentinel-1C launched in 2024 to continue the mission. These satellites alone generate thousands of images daily, many of which contain some degree of radio frequency interference.

The new method's computational efficiency makes it practical for operational use. The researchers successfully processed a full Sentinel-1 interferometric wide-swath image measuring 1,488 by 20,546 pixels using blockwise processing, demonstrating scalability to production environments.

Future Developments

The research team indicated that future work will explore algorithms that can better integrate with their LFM model and sparse parametric estimation scheme. Other researchers continue developing complementary approaches, including preprocessing methods applied to raw radar data before image formation and machine learning techniques that could potentially identify interference patterns automatically.

The growing sophistication of interference mitigation techniques reflects both the importance of SAR imagery for Earth observation and the increasing challenges posed by spectrum congestion as satellite constellations proliferate. Each advance in cleaning corrupted radar images extends the scientific utility of these valuable datasets and improves the reliability of applications that depend on them.

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World’s first: Quantum optical clock tested on underwater autonomous sub - Naval Today

World’s first: Quantum optical clock tested on underwater autonomous sub - Naval Today

Breaking the Surface Barrier: Quantum Clocks Redefine Submarine Endurance

The Royal Navy has demonstrated the first operational quantum optical clock aboard an autonomous submarine—a technological breakthrough that promises to extend underwater patrol durations from weeks to months while maintaining navigational precision measured in meters rather than miles.

On 29 October 2025, the Royal Navy announced successful trials of Infleqtion's Tiqker optical atomic clock integrated into the Excalibur extra-large uncrewed autonomous vehicle. The milestone marks the culmination of decades of quantum research investment and signals a fundamental shift in how submarines will navigate, communicate, and prosecute targets in the GPS-denied undersea battlespace where Western naval superiority has long depended on undetected presence.

The Tyranny of Drift

Submarine navigation has always involved trading stealth for positional certainty. Modern inertial navigation systems, paired with conventional microwave atomic clocks, accumulate drift errors at rates of one to two nautical miles per day of submerged operations. For strategic deterrent patrols, this degradation is manageable through periodic GPS updates at periscope depth. But for extended autonomous operations in contested littorals—where future conflicts will likely be decided—current systems impose unacceptable operational constraints.

The problem lies in fundamental physics. Microwave atomic clocks, including those aboard GPS satellites, measure cesium atom oscillations at approximately 9.2 gigahertz. These clocks drift by roughly 10 nanoseconds per day, which translates to three meters of GPS positioning error. When multiplied by the speed of light, even nanosecond timing errors produce significant navigational inaccuracies.

The Optical Revolution

Optical atomic clocks operate at frequencies approximately 100,000 times higher than microwave clocks—in the visible light spectrum around 500 terahertz—enabling measurement precision orders of magnitude greater than conventional systems. Modern optical clocks achieve systematic uncertainties beyond the 18th decimal place; if such a clock had been running since the Big Bang, it would be at most one second slow today.

The Excalibur trial validated this laboratory performance in operational maritime conditions. By placing a compact optical clock with performance equivalent to a national laboratory-grade time reference directly onboard a submarine, the trial showed how Tiqker can provide a steady "time heartbeat" that smooths out the noise causing navigation drift, enabling submarines to remain submerged, accurate, and hidden for longer durations.

Commander Matthew Steele, Head of Futures in the Royal Navy's Disruptive Capabilities and Technologies Office, characterized the trial as marking "a first critical step towards understanding how quantum clocks can be deployed on underwater platforms to enable precision navigation and timing in support of prolonged operations."

Beyond Navigation: The Integration Cascade

The operational implications extend far beyond route accuracy. Currently, even with advanced conventional methods, submarines need to surface every few weeks to recalibrate their navigation systems. Quantum timing eliminates this vulnerability window while enhancing multiple critical submarine systems simultaneously.

Sonar Processing: Passive sonar arrays tracking quiet targets at long range require precise time-stamping of acoustic returns. Even microsecond timing errors introduce bearing ambiguities that degrade tracking solutions. Quantum-level timing precision enables more accurate target localization and improved multi-static sonar coordination.

Secure Communications: Emerging quantum communication protocols promise higher data rates and two-way capability but require timing synchronization orders of magnitude more precise than current systems provide. The Royal Navy's quantum timing capability creates the foundation for future quantum communication networks.

Fire Control Systems: Tomahawk cruise missiles and other precision weapons require accurate initial position data to activate terrain-matching guidance. Current procedures often necessitate coming to periscope depth for GPS updates before launch—a critical vulnerability in contested environments. Quantum clock-enabled navigation could eliminate this requirement entirely.

The Broader Strategic Context

The Royal Navy's achievement builds upon sustained United Kingdom investment in quantum military applications. In March 2022, HMS Prince of Wales became the first surface warship to deploy quantum timing technology—a laptop-sized atomic clock providing backup timing for the carrier's combat systems during Arctic operations. The Excalibur autonomous submarine now serves as the service's primary quantum technology testbed, with Infleqtion as the first external partner invited to participate in the program.

Meanwhile, peer competitors are advancing rapidly. Chinese researchers successfully tested a drone-mounted quantum sensor system in April 2025 that achieved picotesla precision for submarine detection, overcoming blind zones in low-latitude regions like the South China Sea where Earth's magnetic field runs nearly parallel to the surface. China has invested heavily in quantum navigation, quantum communications for ballistic missile submarines, and superconducting quantum interference devices (SQUIDs) capable of detecting ferrous objects—including submarines—from considerable distances.

The United States is also accelerating quantum navigation development. In August 2025, DARPA awarded Q-CTRL two contracts valued at $24.4 million under its Robust Quantum Sensors program to develop quantum-based navigation technologies. Q-CTRL's Ironstone Opal quantum navigation system recently achieved up to 111 times greater positioning accuracy than high-end inertial navigation systems in flight tests when GPS was unavailable. The Defense Innovation Unit, in collaboration with Vector Atomic and Honeywell Aerospace, has developed the first atomic gyroscope to undergo space qualification, expected to be the first atomic inertial sensor to operate in space.

Implications for American Submarine Operations

The U.S. submarine force must recognize that quantum navigation represents more than incremental improvement—it enables fundamentally new operational concepts. Extended patrols without degraded accuracy, reduced vulnerability during weapon employment, enhanced coordination across domains, and improved tracking in contested acoustic environments become possible.

Quantum navigation is not just technological advancement; it is the key for submarines to achieve ultimate autonomy. As artificial intelligence reduces crew requirements and advanced communication systems mature, quantum navigation completes the architecture for fully autonomous undersea platforms capable of extended independent operations.

The race for quantum advantage in the undersea domain carries profound strategic weight. Quantum technologies—from ultra-sensitive magnetometers to quantum gravimeters that could map seafloors with unprecedented precision—threaten to pierce the veil of stealth that has protected submarines for generations. The nation that first deploys reliable, operationally mature quantum navigation will possess submarines capable of operating longer, striking more precisely, and coordinating more effectively than adversaries—potentially decisive advantages in the precision-strike warfare that would characterize great power conflict.

The Path Forward

Several challenges remain before quantum clocks become standard submarine equipment. The Royal Navy noted that additional performance benchmarking against high-grade time standards will follow the Excalibur trial. Integration with existing combat systems, ruggedization for the full spectrum of submarine operations, and lifecycle cost considerations must all be addressed.

Yet the fundamental question is no longer whether quantum navigation will transform submarine operations, but rather how quickly naval forces can field these capabilities at scale. The silent service has weathered multiple revolutions: nuclear propulsion freed submarines from atmospheric dependence, advanced quieting technology made them acoustically invisible, and sophisticated combat systems transformed them into multi-mission platforms. Quantum navigation represents the next evolution—one measured not in knots or depth ratings, but in the atomic oscillations of light itself.

The quantum of silence has moved from laboratory theory to operational reality. Submarine forces that master this technology first will possess a fundamental advantage in the contested undersea battlespace—one that could prove decisive in maintaining the maritime dominance upon which Western security depends.

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Systems Engineering Enters New Era as SysML v2 Standard Gains Final Approval, AI Integration

Exploring The Ne

xt Frontier of Systems Engineering: SysMLv2

OMG formally adopts next-generation modeling language after eight-year development; Dassault Systèmes leads commercial implementation as AI capabilities promise to ease steep learning curve

The Object Management Group (OMG) formally adopted the Systems Modeling Language (SysML) version 2.0 specification on June 30, 2025, marking a watershed moment for model-based systems engineering after eight years of development. The formal specification was published in September 2025, enabling commercial tool vendors to begin full-scale implementation of a standard that industry leaders believe will fundamentally transform how engineers design complex systems.

The adoption package includes three interconnected specifications: SysML v2.0, the Kernel Modeling Language (KerML) v1.0 that provides its semantic foundation, and the Systems Modeling Application Programming Interface (API) and Services v1.0 that enables interoperability across tools.

First Mover Advantage

Dassault Systèmes is positioned to dominate early adoption, having announced during an October 2024 technical webinar that it will release comprehensive SysML v2 support in its Cameo Systems Modeler portfolio with its 2026x release, scheduled for late November or early December 2024. The company has been running early access programs since the beta specification phase, giving it significant lead time over competitors.

"We definitely have the most powerful solution there in terms of features," said Andrew Lytvynov, SysML v2 Implementation Lead at Dassault Systèmes. "You don't need five tools to do MBSE with SysML v2. You just use Cameo for this."

Other major vendors including IBM, PTC, Siemens, and multiple open-source initiatives have also committed to SysML v2 support. IBM unveiled its Rhapsody Systems Engineering cloud platform in September 2024, leveraging SysML v2 as a foundational technology.

Breaking with the Past

SysML v2 represents a complete architectural departure from its 15-year-old predecessor. Unlike SysML v1, which was built on the Unified Modeling Language (UML) designed for software engineers, v2 is constructed on the Kernel Modeling Language (KerML), which provides formal semantics and was designed specifically for systems engineering.

The most visible innovation is dual syntax support: engineers can work with models using traditional graphical notation (boxes and lines) or a new textual syntax, with full bidirectional synchronization between the two representations. Dassault claims to be the only vendor currently offering true synchronization between syntaxes.

More fundamentally, SysML v2 introduces a standardized expression language that enables automated analysis and verification. Unlike v1, where expressions were tool-specific and non-standard, v2 expressions are universally understood across implementations, enabling engineers to define mathematical relationships, constraints, and dependencies that tools can automatically evaluate.

Requirements can now include formal constraints that evaluate to true or false, moving beyond purely textual descriptions to enable automated verification—a game-changer for complex systems with thousands of requirements.

The Cost of Progress

However, the transition presents substantial challenges. Thomas Baniszewski, Teamwork Cloud Product Manager at Dassault, revealed a striking statistic: SysML v2 models are approximately ten times larger than equivalent v1 models, requiring significant infrastructure upgrades for organizations using server-based collaboration.

"All of these fancy language features like formal grounding, ontological semantics, high degree of expressiveness—it actually comes with a cost that resides at persistency of such models," Baniszewski explained.

The learning curve is equally steep. Lytvynov acknowledged that while the language is more consistent internally, "it's more powerful than v1 but at the same time it means you will need to learn a little more as an expert." Engineers must master not only new syntax but entirely different modeling paradigms, including usage-focused modeling concepts absent from v1.

A March 2024 Department of Defense technical report on SysML v1-to-v2 migration emphasizes that transformation is "lossy"—information will be lost—and requires substantial post-processing to leverage v2 capabilities. Organizations cannot simply run conversion tools; they must undertake expert-led remodeling efforts that could span months or years for large repositories.

AI as the Great Equalizer

The textual syntax that contributes to SysML v2's complexity may paradoxically become its greatest adoption accelerator. Multiple research initiatives are demonstrating that large language models (LLMs) can generate SysML v2 models from natural language specifications, potentially reducing the time to create initial model structures from weeks to minutes.

A September 2024 paper in the INCOSE International Symposium proposed that LLMs can serve as an interpretive layer for SysML v2, reducing dependency on technical expertise traditionally needed for API navigation and model management.

Researchers at multiple institutions introduced SysTemp in June 2025, a multi-agent system that uses LLMs with template generation and parsing agents to automatically create syntactically correct SysML v2 models. An August 2025 study published in a ScienceDirect journal demonstrated that combining LLMs with retrieval-augmented generation and validation engines produces substantial improvements in model correctness.

Practical applications are emerging rapidly. AI assistants could provide real-time syntax help, suggest entire model structures based on natural language descriptions, and help engineers translate v1 thinking patterns into v2's paradigm. ThunderGraph AI has developed systems that build SysML v2 graphs incrementally while validating output, checking for inconsistencies, and ensuring traceability back to source documentation.

"AI LLM specifically interact through text," Lytvynov confirmed when asked about AI integration, "so definitely we understand that and we'll use that as much as possible."

The convergence is particularly timely. Industry thought leaders including Doug Rosenberg, Tim Weilkiens, and Brian Moberley have published guides on AI-assisted MBSE with SysML, providing roadmaps for hardware-software co-design accelerated by AI at every step.

Strategic Implications

"This is an essential contribution to our strategic ambition to evolve systems engineering to a fully model-based discipline," said Ralf Hartmann, president of the International Council on Systems Engineering (INCOSE).

For defense contractors, the timing is critical. The Department of Defense has sponsored transition guidance through its Digital Engineering, Modeling and Simulation office, emphasizing that SysML v2 adoption is essential for following current best practices and supporting strategic missions.

The standardized API specification—the first time an OMG metamodel has included a companion API standard—creates new opportunities for tool integration across aerospace, automotive, defense, and other industries. Model "patching" capabilities specifically address OEM-supplier collaboration by allowing black-box interfaces to be exchanged while protecting intellectual property.

However, the incomplete tool ecosystem poses near-term challenges. Dassault's 2026x release lacks dynamic behavior simulation (coming in 2026 refresh versions) and some third-party integrations. The Unified Architecture Framework (UAF) v2, critical for defense and aerospace, won't have a solid specification until late 2025.

Transition Realities

Dassault is taking a pragmatic migration approach, supporting both v1 and v2 within the same installation "for as long as needed" with no forced timeline. "Some programs will never move to SysML v2 and that's fine," Lytvynov said.

OMG notes that SysML v1.7, adopted in June 2024, will continue being used for several years as industry transitions.

Organizations face strategic choices: early adopters on new programs can gain competitive advantages through advanced capabilities, while teams maintaining legacy systems or in critical program phases may find disruption unacceptable. Those with AI development resources or access to emerging AI-assisted tools may find the transition significantly easier than expected.

"To take advantage of the rich features of the language and API, organizations are encouraged to develop a transition strategy and plan to adopt these technologies and leverage these practices," said Chris Scheiber, Chief Engineer at Lockheed Martin.

As the systems engineering community adapts to this generational shift, the unusual convergence of a more complex modeling language with increasingly capable AI assistance may paradoxically make SysML v2 easier to adopt than its simpler predecessor—transforming what appeared to be the technology's greatest weakness into its most compelling advantage.

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1. Dassault Systèmes and OMG. (2024). "Exploring the Next Frontier of Systems Engineering: SysML v2" [Webinar transcript]. October 2024. Presented by Andrew Lytvynov, Nerijus Šatkauskas, and Thomas Baniszewski.

2. U.S. Department of Defense. (2024). "SysML v1 to SysML v2 Model Conversion Approach." Technical Report, March 2024. Office of the Under Secretary of Defense for Research and Engineering. Available at: https://www.cto.mil/wp-content/uploads/2025/02/SysML-v2-TransitionApproach-1.3.pdf

3. Object Management Group. (2025). "Object Management Group Approves Final Adoption of the SysML V2 Specification." Press Release, July 21, 2025. Available at: https://www.omg.org/news/releases/pr2025/07-21-25.htm

4. Object Management Group. (2025). "About the OMG System Modeling Language Specification Version 2.0." Formal specification, Publication Date: September 2025. Available at: https://www.omg.org/spec/SysML/2.0/About-SysML

5. Systems Modeling Community. (2025). "SysML-v2-Release." GitHub Repository. Available at: https://github.com/Systems-Modeling/SysML-v2-Release

6. OMG MBSE Wiki. (2025). "SysML v2 Transition." Last modified February 20, 2025. Available at: https://www.omgwiki.org/MBSE/doku.php?id=mbse:sysml_v2_transition

7. DeHart, John K. (2024). "Leveraging Large Language Models for Direct Interaction with SysML v2." INCOSE International Symposium, September 7, 2024. Available at: https://incose.onlinelibrary.wiley.com/doi/abs/10.1002/iis2.13262

8. Researchers from multiple institutions. (2025). "SysTemp: A Multi-Agent System for Template-Based Generation of SysML v2." arXiv:2506.21608v1, June 20, 2025. Available at: https://arxiv.org/html/2506.21608v1

9. Simon, Célina. (2025). "Introduction to SysML v2." Sodius Willert Blog, February 7, 2025. Available at: https://www.sodiuswillert.com/en/blog/introduction-to-sysml-v2

10. Obeo. (2025). "SysON 2025.2: Adding New Features to SysON is Our Ongoing Commitment." Company Blog. Available at: https://blog.obeosoft.com/syson-2025-2-adding-new-features-to-syson-is-our-ongoing-commitment

11. ThunderGraph AI. "Automating Model Based Systems Engineering with AI." Company Blog. Available at: https://www.thundergraph.ai/blog/automating-mbse

12. Rosenberg, Doug, Weilkiens, Tim, and Moberley, Brian. "AI Assisted MBSE with SysML." Leanpub. Available at: https://leanpub.com/aim

13. Smith, Jamie. (2024). "Automated Reasoning for SysML v2." Imandra Inc. Blog, September 30, 2024. Available at: https://medium.com/imandra/automated-reasoning-for-sysml-v2-ad7e87addba8

14. Friedenthal, Sanford. (2025). "The Next Generation Systems Modeling Language (SysML v2)." INCOSE presentation, May 2, 2025. Available at: https://www.incose.org/docs/default-source/content-library/the_next_generation_systems_modeling_language-sysml_v2-sfriedenthal-incose_sectoriii.pdf

15. Siemens. (2025). "SysML v2 for modern systems engineering: A practical guide." Teamcenter Blog, June 25, 2025. Available at: https://blogs.sw.siemens.com/teamcenter/sysml-v2-guide/

Note: This article synthesizes information from the Dassault Systèmes technical webinar, OMG official announcements, Department of Defense guidance, academic research papers, and industry analysis. Readers should consult OMG documentation at omg.org for official specification details.

 

Turbulence Dynamics Unlock New Understanding of Ionospheric Radar Clutter

Comparison of the simulated and measured RD spectra for ionospheric inhomogeneities using a perceptual similarity metric. 
(a) AlexNet-based LPIPS results of dataset A. (b) AlexNet-based LPIPS results of dataset B. 
(c) SqueezeNet-based LPIPS results of dataset A. (d) SqueezeNet-based LPIPS results of dataset B. 
(e) VGG-based LPIPS results of dataset A. (f) VGG-based LPIPS results of dataset

Understanding the Validation Results: How Well Does the Simulation Match Reality?

This figure shows the results of comparing computer-simulated ionospheric radar clutter against real-world radar measurements. Think of it as a "report card" for how accurately the turbulence model recreates what actual radar systems observe.

What Are We Looking At?

Each panel (a through f) contains multiple box plots—those blue boxes with whiskers extending above and below. Each box plot represents one simulated radar spectrum compared against all 1,290 real radar measurements collected from actual high-frequency surface wave radar systems.

The figure is organized into three rows, each using a different validation method:

Top row (a, b): AlexNet-based LPIPS metric
Middle row (c, d): SqueezeNet-based LPIPS metric
Bottom row (e, f): VGG-based LPIPS metric

The left column (a, c, e) shows "Dataset A" results, while the right column (b, d, f) shows "Dataset B" results—these represent simulations run under two different initial ionospheric conditions.

How to Read the Box Plots

Each box plot tells you:

• Blue box: The middle 50% of similarity scores when comparing one simulation to all measurements
• Red horizontal line in the box: The median (middle value) similarity score
• Whiskers (vertical lines): The range of typical scores
• Red plus signs: Outlier measurements that are unusually different
• Red circles above boxes: The average similarity score for that spectrum

Higher values = better match between simulation and reality. Values range from about 0.35 to 0.70, where 1.0 would be a perfect match.

Key Findings

1. Consistent Performance Across Methods

All three deep learning methods (AlexNet, SqueezeNet, and VGG) show similar patterns, which indicates the results are robust and not dependent on one particular validation technique. The median values cluster between 0.50 and 0.65 across most spectra.

2. VGG Shows the Strongest Performance

The bottom row (panels e and f) using VGG consistently shows the highest similarity scores, with many median values exceeding 0.60 and some approaching 0.70. This suggests that deeper neural networks better capture the complex texture patterns in ionospheric radar clutter.

3. Low Variability = Stable Simulations

The relatively compact blue boxes (small interquartile ranges) indicate that each simulation consistently matches most of the real-world data—not just a few cherry-picked examples. This consistency demonstrates the model's reliability across diverse ionospheric conditions.

4. Subtle Improvement Trend

Looking across the x-axis (RD Spectrum No.), there's a gentle trend toward slightly higher scores in later spectra. This suggests the simulations progressively capture certain evolving characteristics of ionospheric behavior.

What This Means in Practice

The high LPIPS scores (0.55-0.70 range) indicate that the turbulence-based simulation successfully recreates the visual and structural patterns that radar operators actually observe. This validation is crucial because:

• For radar engineers: The model can predict what types of interference patterns to expect under different conditions
• For system designers: Accurate simulations enable testing of clutter suppression algorithms before expensive field deployments
• For scientists: The strong match confirms that turbulence dynamics correctly explain the physical mechanisms creating ionospheric irregularities

The fact that simulations match real data so well across 1,290 different measurements—representing various times of day, seasons, and space weather conditions—demonstrates this isn't just curve-fitting to limited data. The turbulence model captures fundamental physics governing how the ionosphere behaves.

In essence, this figure provides visual proof that treating the ionosphere as a turbulent fluid system (like Earth's atmosphere or ocean currents) successfully explains the complex interference patterns that plague high-frequency radar systems.

Based on the research paper, there is no indication that the model, source code, or computational artifacts have been made publicly available for independent verification.

What the Paper Does NOT Mention:

• No GitHub repository or code sharing platform links
• No data repository citations (e.g., Zenodo, Figshare, IEEE DataPort)
• No supplementary materials section with simulation code
• No statements about data/code availability policies
• No contact information for requesting research materials beyond author emails

What IS Available:

The paper provides:

• Detailed mathematical formulations (Navier-Stokes equations, RANS, k-ε turbulence model)
• Specific parameter values (drift velocities, Reynolds numbers, domain dimensions)
• Validation methodology descriptions
• 1,290 measured RD spectra were used, but no mention of public access

Reproducibility Concerns:

Limited reproducibility without:

1. Simulation code: The specific implementation of RANS/k-ε equations with ionospheric boundary conditions
2. Measured radar data: The 1,290 validation spectra from HFSWR campaigns
3. Pre-trained models: The LPIPS validation networks and their configuration
4. Preprocessing pipelines: How raw radar data was converted to RD spectra for comparison
5. Initial conditions: Exact parameters for "Dataset A" and "Dataset B" simulations

Standard Practice vs. This Study:

Modern computational research increasingly requires:

• IEEE policy: Encourages but does not mandate code/data sharing
• Reproducibility standards: Leading journals now expect public repositories
• Community norms: Computational fluid dynamics studies often share meshes, solver settings, and validation datasets

This study appears to follow traditional publication practices where detailed methods are described but implementation artifacts remain with the research group.

Options for Verification:

If you need to reproduce or verify this work:

1. Contact authors directly:

   • Yuanbiao Li: ybuleo@foxmail.com
   • Lei Yu: yu.lei@hit.edu.cn
   • Yinsheng Wei: hitweiysgroup@163.com

2. Request specific materials:

   • Simulation source code
   • Configuration files for COMSOL/OpenFOAM/custom solver
   • Sample RD spectra from validation dataset
   • LPIPS model architectures and weights

3. Partial replication:

   • The mathematical framework is fully specified
   • Commercial CFD software (COMSOL Multiphysics, ANSYS Fluent) can implement RANS/k-ε models
   • LPIPS is open-source: https://github.com/richzhang/PerceptualSimilarity
   • Could replicate methodology with different radar datasets

Recommendation:

For independent verification, direct communication with the Harbin Institute of Technology research group is necessary. The Chinese research institution may have different data sharing policies than Western universities, and military/dual-use radar applications may limit data release even for civilian research.

The lack of publicly available artifacts is a significant limitation for a 2025 publication, especially one making novel methodological claims. Ideally, the validation dataset (even if anonymized/subset) and core simulation code should be shared to enable the scientific community to build upon this work. Perhaps the IEEE should make this a condition of publication.

 

Novel simulation model based on fluid dynamics principles accurately recreates ionospheric irregularities that interfere with high-frequency radar systems

Researchers at Harbin Institute of Technology have developed a groundbreaking approach to modeling ionospheric clutter that combines classical fluid dynamics with deep learning validation, offering new insights into how turbulent plasma structures in Earth's upper atmosphere interfere with high-frequency surface wave radar (HFSWR) systems.

The study, published in IEEE Transactions on Geoscience and Remote Sensing, represents a significant departure from previous ionospheric clutter models that relied primarily on statistical characterizations of radar interference patterns. Instead, the research team led by Yuanbiao Li, Lei Yu, and Yinsheng Wei applied the principles of turbulence dynamics to explain the microphysical mechanisms underlying small-scale ionospheric irregularities.

From Statistical Models to Physical Mechanisms

The ionosphere—Earth's electrically charged upper atmosphere extending from approximately 60 to 1,000 kilometers altitude—serves dual roles in radar operations. It enables long-range communication for skywave radar systems while simultaneously generating problematic clutter for surface wave radar. Understanding and predicting this clutter has been a persistent challenge for radar engineers.

"Even during quiet conditions, the ionosphere exhibits dynamic, small-scale irregularities that interact with radio waves, causing rapid signal fluctuations, discreteness, and spectral broadening," the researchers explain in their paper. These irregularities manifest as five distinct clutter types in radar data: lamellar distribution, dot distribution, spatial correlation, distance-dependent, and target-like patterns.

Previous modeling approaches treated ionospheric clutter primarily as a statistical phenomenon, focusing on amplitude and phase characteristics without fully explaining the underlying physical processes. The new model takes a fundamentally different approach by treating the ionosphere as a turbulent fluid system governed by the Navier-Stokes equations—the same mathematical framework used to describe atmospheric weather patterns and ocean currents.

Turbulence at the Edge of Space

The research team employed Reynolds-averaged Navier-Stokes (RANS) equations combined with the k-ε turbulence model to simulate ionospheric inhomogeneities under various flow conditions. By varying key parameters—particularly drift velocity (10-50 m/s) and Reynolds number (400-4,000)—the simulations revealed how ionospheric irregularities form and evolve through distinct stages.

The Reynolds number, a dimensionless quantity that characterizes the ratio of inertial forces to viscous forces in a fluid, proved critical to understanding ionospheric behavior. At low Reynolds numbers (around 400) with drift velocities of 10 m/s, the ionosphere maintains relatively laminar flow patterns. As the Reynolds number increases to 4,000 and drift velocities reach 50 m/s, the flow transitions through vortex formation to fully developed turbulence.

"When the Reynolds number reaches 4,000 and the free drift velocity of ionospheric inhomogeneities reaches 50 m/s, the initially homogeneous sheet flow tends to form multiple smaller scale vortices," the researchers observed. This transition manifests in radar data as dot distribution fine structures embedded within broader lamellar distributions—a fractal-like pattern consistent with the self-similar nature of turbulence.

The simulation covered a horizontal range of 5 kilometers and a vertical range of 10 kilometers, representing the E- and F-layers of the ionosphere where most radar-relevant irregularities occur. These layers, located between 90-140 km and extending to 1,000 km respectively, contain molecular and atomic ions that respond to electromagnetic fields and neutral atmospheric winds.

Validation Through Deep Learning

To validate their turbulence-based simulations, the research team took the innovative step of applying deep learning metrics traditionally used in computer vision to compare simulated and measured radar data. They analyzed 1,290 range-Doppler spectra collected from actual HFSWR measurements, each representing 200 range bins and 512 Doppler frequency bins.

Traditional image quality metrics like peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) showed moderate agreement between simulated and measured data. However, these metrics demonstrated limitations in capturing the perceptual similarities crucial for validating complex signal patterns.

The researchers achieved more compelling validation using the Learned Perceptual Image Patch Similarity (LPIPS) metric, which employs deep neural networks to assess perceptual similarity in ways that align more closely with human judgment. Testing LPIPS with three different neural network architectures—AlexNet, SqueezeNet, and VGG—the team found consistently high similarity scores, with median values exceeding 0.6 and peak scores approaching 0.7.

"VGG-based LPIPS boxplots show the strongest performance among all metrics," the paper notes. "Median values consistently exceed 0.6, with several spectra near or above 0.7." The reduced variability in VGG-LPIPS scores indicated high consistency in simulation quality across different ionospheric conditions.

Five Flavors of Ionospheric Interference

The validated simulations revealed distinct clutter types corresponding to different stages of turbulence development. Target-like clutter appears during laminar flow conditions when Reynolds numbers are low and drift velocities slow. This pattern exhibits properties similar to actual radar targets—including directionality and concentration in spatial and range domains—but with dispersed power that can trigger false alarms in detection algorithms.

Lamellar distribution clutter, characterized by high concentrations of ionospheric ions in certain layers, manifests as strong energy aggregation occupying multiple range-Doppler resolution units. This pattern can persist as a quasi-stable state during turbulent conditions.

As flow transitions to the vortex stage, distance-dependent clutter emerges in regions of slowly varying ion concentrations between ionospheric layers, typically occupying multiple resolution units with Doppler frequencies close to zero. Spatial correlation clutter forms in regions of rapidly changing concentrations, producing dispersed power with larger Doppler frequencies and characteristic spatial distributions.

Finally, dot distribution clutter appears as small-scale structures embedded within lamellar patterns during fully developed turbulence. These represent the internal self-similar structure of larger clutter patterns—supporting the application of fractal theory for clutter suppression strategies.

Sensitivity Analysis and Model Limitations

The research team conducted extensive sensitivity analysis to understand how parameter uncertainties affect model predictions. Testing perturbations of 5% and 10% to drift velocity and Reynolds number, they found distinct error patterns for laminar versus turbulent regimes.

For laminar flow conditions, 5% changes in drift velocity produced localized errors confined to shear boundaries, while turbulent flow exhibited broader error propagation aligned with vertical structures. Horizontal errors dominated under drift velocity perturbations—reflecting advection-driven dynamics—whereas vertical errors intensified with Reynolds number changes, indicating stratification-modulated turbulence.

"This anisotropy underscores the importance of multiperspective radar observations to disentangle parameter-specific effects in real-world clutter data," the researchers emphasize.

The study acknowledges several limitations. The RANS model showed reduced accuracy at very high Reynolds numbers, where direct numerical simulation (DNS) or large eddy simulation (LES) would better resolve subrange dynamics. Deep learning validation metrics, while demonstrating strong perceptual alignment, exhibited sensitivity to image scaling. The team also notes fundamental challenges in solving three-dimensional Navier-Stokes equations that constrain model optimization.

Implications for Radar Design and Space Physics

The turbulence-based approach offers practical benefits for HFSWR system design and operation. By accurately simulating different clutter types under varying ionospheric conditions, the model provides a predictive tool for developing adaptive signal processing strategies. The correlation between clutter characteristics and ionospheric conditions suggests promising avenues for real-time clutter mitigation.

The research also contributes to broader understanding of ionospheric physics. The validation of small-scale turbulent mechanisms complements existing models of large-scale field-aligned irregularities based on geomagnetic field models like the International Geomagnetic Reference Field (IGRF). While IGRF-based approaches successfully characterize macroscale stability that enables coherent skywave propagation, the turbulence model explains the chaotic microscale dynamics that generate diffuse clutter patterns.

"The chaotic dynamics of these substructures, unresolved in IGRF-based analyses, directly explain the diffuse clutter patterns observed in HFSWR systems," the researchers note.

This complementary relationship between large-scale stability and small-scale chaos proves essential for optimizing both skywave and surface-wave radar designs. Understanding when and where turbulent conditions develop allows radar operators to anticipate interference patterns and adjust system parameters accordingly.

Future Directions

The research team plans to extend their work in several directions. Future studies will implement more sophisticated simulation techniques including DNS and LES to better capture high Reynolds number turbulence. The model's scope will expand to incorporate additional ionospheric variables such as magnetic field variations, solar activity indicators, and seasonal patterns.

The team also intends to investigate the model's applicability across diverse frequency ranges and radar configurations. Current validations focused on typical HFSWR operating frequencies, but ionospheric effects vary significantly across the electromagnetic spectrum.

Integration of real-time ionospheric observations could enable predictive capabilities, allowing radar systems to anticipate clutter conditions based on current space weather. Such integration would require automated data assimilation pipelines connecting ionosonde measurements, satellite observations, and ground-based radar to the turbulence simulation framework.

"Continued refinement of simulation models through integration of diverse observational data is expected to significantly improve HFSWR system reliability and operational effectiveness," the researchers conclude.

The work demonstrates how classical physics principles, modern computational methods, and machine learning validation can combine to solve practical engineering challenges while advancing fundamental scientific understanding. As space weather monitoring capabilities improve and computational resources expand, turbulence-based ionospheric modeling may become standard practice for radar system design and optimization.

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