Hypersonic glide vehicles evade radars. Learn how multispectral sensing, AI, and SkyRadar’s stealth extensions prepare defense for this evolving threat.
In our previous article, we explored why modern defense must adapt to swarming drones and stealth formations. But swarms are only one part of the emerging threat landscape. At the higher end of speed and sophistication lie hypersonic glide vehicles (HGVs)—a technology that is rewriting the rules of detection and tracking.
A recent academic review by Yee Wei Law and colleagues at the University of South Australia and the SmartSat Cooperative Research Centre provides unprecedented insights into this subject. Detecting and tracking HGVs is an area of active and ongoing research, with significant ambiguities and knowledge gaps still being explored. At SkyRadar, we will continue to follow these developments closely and share updates in our series of articles.
What Makes HGVs So Difficult to Detect
Unlike traditional ballistic missiles, HGVs are launched into near space and then glide unpowered toward their targets. Their defining features are:
-
Hypersonic speeds (faster than Mach 5)
-
Near-space trajectories, skimming the atmosphere rather than soaring high like ICBMs
-
High manoeuvrability, allowing unpredictable course changes
This combination enables HGVs to slip through the detection nets of both terrestrial radars and space-based sensors designed for ballistic arcs.
The Role of Infrared and Multispectral Sensing
One promising avenue for HGV detection is space-borne electro-optical and infrared (EO/IR) sensors. As HGVs glide, their surface heats to several thousand Kelvins, producing strong infrared signatures. However, detecting these signals is not straightforward:
-
HGVs appear dimmer and smaller in IR images compared to ballistic missiles.
-
They often occupy less than 0.1% of the image area, requiring advanced small target detection algorithms.
Deep learning techniques such as U-Net architectures and feature pyramid networks are being applied to enhance detection reliability. Research also points to the potential of multispectral sensing, combining IR with ultraviolet emissions from shock layers, though this remains underexplored.
Radar and the Plasma Sheath Problem
Radar remains essential because it provides range and velocity data—something EO/IR lacks. Yet HGVs generate a plasma sheath during hypersonic flight, which scatters and attenuates radar signals. This makes their radar cross section unpredictable.
Two proposed workarounds include:
-
High-frequency radar (Ka-band and above), though current technology is not fully mature.
-
Low-frequency HF over-the-horizon radar, which can detect the plasma sheath indirectly.
Data fusion between ground-based radar and satellite EO/IR constellations appears necessary for resilient detection.
Tracking Algorithms and Knowledge Gaps
Even if an HGV is detected, tracking its trajectory is another challenge. Traditional constant-velocity models fail due to the vehicle’s manoeuvrability. Academic proposals include:
-
Stochastic estimation filters such as Interacting Multiple Models (IMMs)
-
Machine learning methods, from LSTMs to transformers, to predict nonlinear trajectories
However, without real-world flight data, validation of these models remains uncertain.
Training for the Next Generation of Radar Operators
The research underscores the complexity of hypersonic defense. Detecting and tracking HGVs will require:
-
Multispectral sensors (IR, UV, radar)
-
Satellite constellations with distributed tracking
-
Resilient data fusion systems
SkyRadar’s Stealth Detection Extensions to its SkySim radar simulator are designed precisely for this kind of training environment. They allow operators, engineers, and students to experiment with scenarios involving stealthy and hard-to-detect threats—such as hypersonic vehicles—within a safe, controllable simulation space.
With these extensions, learners can explore how advanced radars respond to low-observable signatures, how multispectral data fusion works in practice, and how to apply robust tracking algorithms in the face of evasive manoeuvres.
SkyRadar's Military Training Suite
Hypersonic glide vehicles represent one of the most disruptive developments in modern warfare. They combine stealth-like evasion with speeds that compress decision time to seconds. Research shows that detection will hinge on multispectral sensing, AI-driven small-target detection, and integrated space–ground architectures.
For defense communities, the path forward is clear: invest in new sensor constellations, train personnel on stealth and hypersonic scenarios, and build resilient systems that can adapt as threats evolve.
SkyRadar’s SkySim with stealth detection extensions brings this future into the classroom and the lab—preparing radar professionals for the age of hypersonics.
Let's talk
Stay tuned to be always the first to learn about new use cases and training solutions.
Or simply talk to us to discuss your project.
References
- Detecting and tracking hypersonic glide vehicles: A cybersecurity-engineering analysis of academic literature (2023), by Yee Wei Law, John Joshua Gliponeo, Dilpreet Singh, John McGuire, Jiajun Liang, Sook-Ying Ho and Jill Slay, in: Vol. 18 No. 1 (2023): Proceedings of the 18th International Conference on Cyber Warfare and Security (ICCWS 2023)
