Raydar: A Ray-Tracing–Driven Framework Enabling Pattern-Based Recognition in ISAC Radar

Research Note

Integrated Sensing and Communication (ISAC) is a core technology for Beyond 5G/6G, where communication waveforms are reused for sensing. Under realistic long-range conditions, recognition performance is strongly affected by angle-of-arrival (AoA) mismatch and by geometry-dependent radar cross section (RCS) variation. Empirical data collection is heavily constrained by regulation and privacy, and existing simplified analytical models cannot faithfully reproduce these distortions.

We introduce Raydar, a ray-tracing (RT)-driven simulation and recognition framework. Raydar reproduces the full ISAC sensing pipeline through six modular components — environment generation, transmit waveform generation, channel construction, received-signal synthesis, Range–Doppler (RD) map formation, and target recognition — and generates realistic waveforms and RD maps without any base-station hardware. Ray tracing brings geometry- and material-dependent reflections directly into the simulation.

Using Raydar, we show that vehicle targets exhibit strong geometry-driven RCS variation and that AoA mismatch further suppresses matched-filter (MF) responses, making amplitude-based discrimination unreliable beyond 100 m. Yet the spatial patterns in the RD map remain class-distinct, and a lightweight CNN trained on CFAR-extracted RD patches substantially outperforms threshold-based methods — particularly for weak-RCS vehicle targets.