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.
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.
Phased/Pulse-Doppler Active Radar Operations Simulator — real-time GPU (CUDA) radar DSP: matched filter, range-Doppler, CFAR, with an OpenGL/ImGui tactical display.
Decode mmWave radar and synchronized multi-sensor sessions or live streams into Python training and real-time inference pipelines backed by Rust.
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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