Standardizes the metrology diagnostic suite for high-fidelity focus and
consistent dB scaling.
Core Improvements:
- Signal Processing: Switched Range and Doppler FFTs to Hann windowing,
reducing peak width by ~50% for sharper target focus (Iteration 27).
- Visualization Style: Implemented a fixed-scale MATLAB-style Jet colormap
(-5 to 45 dB). Disabled per-frame auto-scaling to preserve temporal
intensity consistency and physical R^-4 decay.
- Calibration: Applied a -68.0 dB System Gain Offset in post-processing
(calculated via statistical analysis of raw power levels in Iter 28)
to align noise floors (~7 dB) and target peaks (~41 dB).
- Pipeline: Standardized these focus and calibration settings across both
the testbench (test_shenron.py) and packaging pipeline (data_to_mcap.py).
- Docs: Verified and restored gemini.md at the repository root as the
master project context.
This milestone (Iteration 29) finalizes the visual interpretability of the
physics-based Shenron radar engine.
Restores high-fidelity physical realism to the C-Shenron radar engine by
aligning the synthesis pipeline with the pure Radar Range Equation.
Core Improvements:
- Physics: Restored 1/R^4 power-delay law (1/R^2 voltage) in Sceneset.py
and heatmap_gen_fast.py. Stripped legacy 1/1000 normalizations and
R^2 area-growth workarounds.
- Geometry: Fixed FFT index asymmetry in radar_processor.py, achieving a
perfectly symmetric 120° FOV sector.
- Metrology: Implemented "Radar Blue" (Viridis) 120° fan-projection for
diagnostic Range-Azimuth heatmaps.
- Automation: Integrated RD/RA/CFAR heatmap persistence into the
automated simulation-to-MCAP pipeline (data_to_mcap.py).
- Docs: Comprehensive update of intel/ directory, including Iterations 17-26
and the Physics/Symmetry Milestone deep-dive.
This milestone ensures that target brightness and spatial positioning
correctly mimic real-world TI AWRL1432 radar hardware.
- Engine: Modified CFAR and Processor to extract raw Range-Doppler and Range-Azimuth energy heatmaps.
- Visuals: Integrated high-fidelity Matplotlib colormapping (Viridis/Magma) for Foxglove image streaming.
- Data: Implemented 32-bit raw .npy persistence and JSONL telemetry for frame-level SNR analysis.
- Tools: Added a dedicated verification utility for end-to-end signal flow validation.
- Docs: Comprehensive documentation for the new 'Radar Lab' architecture in /intel/.
RUSHIL AMBARISH KADU