Professional Pulse-Doppler Radar Simulation Platform
Physics-Based • AI-Enhanced • Imaging Radar • Open Source
Features • Quick Start • Shortcuts • Docs • Citation
RadarSim is a scientifically-validated radar simulation engine for education, research, and professional training. Built with NumPy/Numba for performance and PyQt6 for a modern UI.
- Pulse-Doppler Engine: Signal-level processing (CPI, MTI, FFT) referenced from Richards (2005).
- Advanced Tracking: Extended Kalman Filter (EKF) with polar coordinates.
- Electronic Warfare: DRFM Jamming (RGPO/VGPO) and Frequency Agility.
- Sensor Fusion: Networked Radar & Strobe Triangulation.
- Imaging: High-resolution SAR/ISAR algorithms.
- AI/Cognitive Control: Dynamic logic referenced from Haykin (2006).
Key Validation: Radar Equation calculations achieve ±0.005 dB accuracy vs. Skolnik reference values.
Figure 1: Main Plan Position Indicator (PPI) display showing detected targets.
| Feature | Implementation | Reference |
|---|---|---|
| Radar Equation | Monostatic/Bistatic with Numba JIT | Skolnik, Ch. 2 |
| Atmospheric Attenuation | ITU-R P.676-12 (O₂ + H₂O) | IEEE Std |
| Swerling RCS Models | Types 0-4 fluctuation | Swerling (1960) |
| Monopulse Tracking | Sum/Difference patterns, sub-beamwidth accuracy | Phase 20 |
| 3D Terrain Masking | 4/3 Earth refraction, LOS shadowing | ITU-R P.526 |
| Feature | Details |
|---|---|
| Ground Clutter | Weibull distribution, σ⁰ coefficients |
| Sea Clutter | GIT Model, Douglas sea states (1-6) |
| Rain Physics | ITU-R P.838 Attenuation + Marshall-Palmer Clutter |
| MTI Filtering | Velocity threshold, slow-mover rejection |
| Technique | Type | Status |
|---|---|---|
| Noise Jamming | ECM | ✅ |
| DRFM Repeater | ECM | ✅ |
| RGPO/VGPO Deception | ECM | ✅ |
| Frequency Agility | ECCM | ✅ |
| Burn-Through Display | ECM Strobe | ✅ |
| Feature | Implementation | Reference |
|---|---|---|
| SAR (RDA) | Vectorized 5-stage Range-Doppler Algorithm | Cumming & Wong (2005) |
| ISAR | Cross-range imaging via target rotation | Chen & Ling (2002) |
| Resolution | Δr = c/(2B) = 1.5m, Δa = D/2 = 0.5m | Verified |
| Feature | Description |
|---|---|
| Coverage Analysis | 2D Pd map from multi-radar network |
| Blind Zone Detection | Flood-fill connected component analysis |
| Attack Planning | 3 difficulty levels (Easy/Medium/Hard) |
| Low-Pd Routing | Greedy corridor navigation |
| Jammer Deployment | Optimal DRFM positioning |
| Scope | Description |
|---|---|
| PPI | Plan Position Indicator with phosphor decay |
| B-Scope | Range vs Azimuth (AESA style) with ECM strobes |
| A-Scope | Amplitude vs Range with CFAR hover visualization |
| RHI | Range-Height Indicator (elevation) |
| 3D Tactical | OpenGL terrain with target spheres |
| SAR Viewer | Real physics-based Synthetic Aperture Radar imaging |
| SAR Imaging | 3D Tactical Map |
|---|---|
 |  |
| Real-time SAR formation (Range-Doppler) | 3D situation awareness |
| RHI Scope | A-Scope Analysis |
|---|---|
 |  |
| Elevation scanning (Range-Height) | CFAR threshold visualization |
| Tool | Function |
|---|---|
| Ambiguity Diagram | PRF vs Range/Velocity trade-off |
| ROC Curves | Pd vs Pfa for Swerling models |
| SNR Histogram | Detection strength distribution |
Ambiguity Diagram • ROC Curves • Real-time SNR Statistics
- RandomForest Classifier trained on synthetic radar data
- Classes: Drone 🛸, Fighter Jet
✈️ , Missile 🚀 - Real-time inference with confidence scoring
# Clone repository
git clone https://github.com/SpaceEngineerSS/RadarSim.git
cd RadarSim
# Create virtual environment
python -m venv .venv
.venv\Scripts\activate # Windows# source .venv/bin/activate # Linux/Mac# Install dependencies
pip install -r requirements.txt
# Run application
python run_gui.py| Key | Action |
|---|---|
Space | Play/Pause simulation |
R | Reset (stop) simulation |
1 | Switch to PPI Scope |
2 | Switch to RHI Scope |
3 | Switch to 3D Tactical |
4 | Switch to 4th tab |
F11 | Toggle fullscreen |
Ctrl+O | Load scenario |
Ctrl+Shift+S | Save scenario |
Ctrl+R | Start recording |
RadarSim/
├── run_gui.py # Main entry point (PyQt6 GUI)
├── headless.py # Headless simulation runner
├── batch_run.py # Batch scenario executor
├── requirements.txt # Dependencies
├── scenarios/ # YAML scenario files (10 scenarios)
│ ├── f16_vs_sa6.yaml
│ ├── drone_swarm_saturation.yaml
│ ├── naval_battlegroup.yaml
│ └── ...
├── src/
│ ├── physics/ # Core physics (radar_equation, clutter, ecm)
│ ├── signal/ # Signal processing (cfar, pulse_doppler, antenna)
│ ├── tracking/ # Target tracking (EKF, monopulse, track manager)
│ ├── simulation/ # Simulation engine & network manager
│ ├── ui/ # PyQt6 GUI (PPI, B-Scope, RHI, A-Scope, 3D)
│ ├── advanced/ # SAR/ISAR, AI Director, Sensor Fusion, ECCM, LPI
│ └── ml/ # AI classification pipeline
├── models/ # Trained ML models
├── docs/ # Documentation
└── tests/ # 217 unit tests
| Document | Description |
|---|---|
| Physics Engine | Radar equation, Monopulse, Ambiguity |
| Signal Processing | CFAR, MTI, SAR algorithms |
| User Guide | GUI walkthrough and Advanced features |
- Skolnik, M.I. — Radar Handbook, 3rd Ed., McGraw-Hill, 2008
- Richards, M.A. — Fundamentals of Radar Signal Processing, 2nd Ed., McGraw-Hill, 2014
- IEEE Std 686-2017 — Radar Definitions
- ITU-R P.676-12 — Attenuation by Atmospheric Gases
- Cumming & Wong — Digital Processing of SAR Data, Artech House, 2005
- Chen & Ling — Time-Frequency Transforms for Radar Imaging, Artech House, 2002
- Bar-Shalom, Y. — Estimation with Applications to Tracking, Wiley, 2001
- Julier & Uhlmann — Non-divergent Estimation Algorithm, ACC, 1997
- Schleher, D.C. — Electronic Warfare in the Information Age, Artech House, 1999
- Poisel, R. — Electronic Warfare Target Location Methods, Artech House, 2012
MIT License - See LICENSE for details.
If you use RadarSim in academic work, please cite:
@software{radarsim2025,
title = {RadarSim: Physics-Based Pulse-Doppler Radar Simulation},
author = {RadarSim Contributors},
year = {2025},
url = {https://github.com/SpaceEngineerSS/RadarSim}
}Mehmet Gümüş
- 🌐 Website: spacegumus.com.tr
- 🐙 GitHub: @SpaceEngineerSS
Built with ❤️ for the Radar Community