Multi-Object Tracking with Transformer Neural Networks on Range-Doppler Maps
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Updated
May 17, 2025 - Python
Multi-Object Tracking with Transformer Neural Networks on Range-Doppler Maps
Code and additional information to our paper "RACPIT: Improving Radar Human Activity Classification Using Synthetic Data with Image Transformation"
Modeling and simulation of FMCW radar principles, covering waveform generation, detection, and Range-Doppler map analysis for automotive and robotics applications.
MATLAB simulation of AFDM-based integrated sensing and communication for high-mobility wireless and radar sensing.
Standalone mmWave ADC compression/decompression, deterministic radar DSP, and classical tracking with Rust-backed Python APIs.
Reverse engineering and experimental tooling for the Seeed Studio MR60BHA2 / ADT6101P 60 GHz FMCW radar: coherent range-Doppler streams, guarded firmware patching, and ESP32-C6 web visualization.
Real-time radar perception pipeline for automotive systems with multi-target detection, velocity estimation, and spatial localization.
SAR imaging toolbox providing Time‑Domain Back‑Projection and Range‑Doppler backends, with Python, Numba, and GPU‑ready implementations. Includes analysis modules, despeckling filters, and a complete test and benchmark suite.
Phased/Pulse-Doppler Active Radar Operations Simulator — real-time GPU (CUDA) radar DSP: matched filter, range-Doppler, CFAR, with an OpenGL/ImGui tactical display.
“C++17 radar signal processing pipeline: pulse compression, range-Doppler FFT and CA/OS-CFAR target detection”.
Radar target generator (DRFM) on ZCU208 RFSoC: delay/Doppler/gain-controlled false echoes, live web GUI, verified against radar on SDR. Saab summer project 2026.
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