SkyRadar’s extended SkySim simulator trains radar operators and ATSEP students in stealth detection, swarm analysis, passive radar, and electronic warfare—offering scalable, AI-enhanced, and classroom-ready modules for real-world readiness.
What Is SkySim?
SkyRadar’s SkySim simulator has long stood as a key tool for radar education and military as well as civil ATSEP qualification. It makes radar operations tangible, letting learners simulate everything from signal transmission to waveform processing. SkySim is often used alongside FreeScopes, SkyRadar’s real-time radar visualization suite, where students can observe and interact with radar data in A-scope, B-scope, or PPI formats—complete with features like frequency agility, pulse compression, and Doppler filtering.
With this foundation in place, SkyRadar now expands SkySim into new dimensions: electronic warfare and stealth detection. A powerful suite of simulation modules enables users to detect and analyze low-observable threats using a combination of traditional and AI-enhanced techniques, all within an immersive, risk-free environment.
RCS Awareness and Multi-Band Operation
The journey begins with RCS (Radar Cross Section) awareness, a fundamental concept in understanding stealth technology. In this introductory module, students learn how factors like aircraft shape, radar frequency, and aspect angle influence detectability.
They toggle between frequencies / bands to observe how radar returns vary. Stealth aircraft are compared directly to conventional ones, following the same flight path to visualize the differences in radar signatures. Students also experiment with a basic AI classifier, trained on simulated multi-band returns, which estimates the likelihood that a detected object is stealth. This module forms the theoretical and practical foundation for everything that follows.
Multi-Static and Passive Radar Techniques
The second module introduces geometry-based detection and passive radar. Students learn that stealth’s effectiveness is highly angle-dependent—what’s invisible in one geometry might be clearly visible in another.
By operating bi-static or multi-static radar configurations, trainees learn how to position receivers to view a target from multiple sides. The module also allows for passive detection, using third-party emitters like GSM towers or TV broadcasts as illuminators. Using Time-Difference-of-Arrival (TDOA) and cross-correlation techniques, students detect and track stealth targets even when no radar emissions are used. An AI-based sensor fusion engine further assists by combining passive and active detections into a single, reliable picture.
Swarm Detection and Complex Target Analysis
Stealth is no longer limited to fighter jets. The third module tackles one of the most modern and complex threats: drone swarms. These consist of dozens of low-RCS targets flying in formation—nearly invisible to classic radar.
Students use micro-Doppler analysis to identify spinning rotors or moving parts, even when power returns are minimal. They apply track-before-detect techniques and clustering logic to separate drone swarms from weather clutter or terrain echoes. A convolutional neural network (CNN) trained on synthetic micro-Doppler spectrograms provides automated assistance, flagging drone-like activity with high precision.
This module helps learners develop the skill to distinguish organized low-observable threats from environmental noise, an essential competency in modern airspace security.
Full-Spectrum Counter-Stealth Operations
The fourth module combines everything learned so far into a unified simulation of realistic stealth tactics and countermeasures. Stealth aircraft now operate in terrain-masked modes, use deceptive jamming, and exploit low-altitude clutter.
Students must respond using a combination of radar bands, SAR/ISAR imaging, micro-Doppler signatures, and geometric triangulation. A rule-based sensor fusion engine helps apply decision logic, while an AI deep learning system (CNN + LSTM) processes complex time-series radar data. This module mirrors actual combat radar operation and electronic warfare conditions—requiring layered responses, interpretation of conflicting data, and strategic prioritization of sensor feeds.
Upcoming Module: Polarimetric Detection
Scheduled for release in 2027, the fifth module brings polarimetric radar detection into the simulation suite. Students will train using horizontal (H), vertical (V), and circular (RHCP/LHCP) polarization modes to detect polarization-dependent reflections from stealth aircraft.
By alternating between polarization states, learners expose subtle glints from wing edges, cavities, and anisotropic coatings—features that often evade detection in conventional setups. Analytical tools such as co- and cross-polarization ratio analysis, as well as AI-based classifiers, help automate this process and maintain track continuity even when one polarization channel fades.
This module is fully integrated with SkySim’s antenna and polarization training material, creating a seamless learning experience across disciplines.
Realism, Safety, and Scalability
All stealth targets in these modules are synthetic but technically plausible, meaning they are not based on classified data—yet they provide realistic behaviors and visualizations. Each scenario is fully modular and scalable, supporting individual learners or full classroom deployments.
Preparing for the Radar Challenges of Tomorrow
SkyRadar’s extended SkySim suite represents a step change in radar education. No longer limited to visualizing signal reflections or plotting range, students now learn how to diagnose low-observable threats, work under electronic countermeasures, and build situational awareness from weak or conflicting signals.
Whether the goal is ATSEP certification, military radar operation, or advanced research and development, these new modules prepare learners for exactly the kinds of threats they will face in real-world radar environments.
With SkySim, radar training is no longer just about how radar works. It’s about how to think like a radar operator—when stealth, deception, and uncertainty are part of the equation.
