Simulator/intel/radar/archive/shenron_integration.md

[Plan] Shenron Model-Based Development (MBD) Integration

This plan shifts the integration to a Model-Based Development architecture. We will treat the isolated Shenron logic as a "strictly defined black box" (similar to a Simulink model) and enhance it to output "Rich" radar data.

User Review Required

[!IMPORTANT] Rich Point Cloud: We will modify the extraction logic to output more than just geometry. The new format will be: [x, y, z, velocity, SNR, RCS_estimate].

Source of Data:

• SNR: Extracted from the peak magnitude in the Range-Doppler heatmaps.
• RCS: Back-calculated from the return power logic in Sceneset.
• Noise: Sampled from the simulated noise floor in ConfigureRadar.

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Phase 1: Environment & Dependency Setup

... (Previously defined)

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Phase 2: The "Black Box" Wrapper & Rich Extraction

[MODIFY] ISOLATE/sim_radar_utils/radar_processor.py

Modify convert_to_pcd() to preserve the magnitude of the detected peaks.

• Current: Returns [x, y].
• New: Returns [x, y, z, velocity, magnitude].

[NEW] ISOLATE/model_wrapper.py

The wrapper handles "Rich" data synthesis and telemetry:

1. Call Sceneset for ground-truth physical power (RCS).
2. Call HeatmapGen for the noisy ADC stream.
3. Call RadarProcessor to extract detected peaks.
4. Telemetry: New get_signal_metrics() method exports SNR and noise floor data.

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Phase 3: Modular Pipeline Integration

The automated pipeline now supports concurrent hardware simulation.

[MODIFY] scripts/generate_shenron.py

Loop through multiple hardware profiles:

• AWRL1432: Short-range, high-resolution.
• RadarBook: Long-range standard.

[MODIFY] scripts/data_to_mcap.py

UnifiedTopic Management:

• /radar/native
• /radar/awrl1432
• /radar/radarbook

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Verification Plan

Automated Verification

1. Pilot Run: Record a 100-frame scenario with semantic LiDAR.
2. Post-Process: Run python scripts/generate_shenron.py.
3. Check Output: Verify data/<session>/awrl1432/ and radarbook/ contain 100 .npy files each.

Manual Verification

1. Run python scripts/data_to_mcap.py.
2. Open the MCAP in Foxglove and verify that the /radar/radarbook/metrics topic shows stable telemetry curves.