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CARLA ? C-Shenron based Simualtor for Sensor data generation.
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Simulator/intel/radar/metrology_suite/README.md

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Radar Metrology Suite: Implementation Hub 🛰️

This directory serves as the source of truth for the C-SHENRON Radar Metrology Suite. The goal of this suite is to transform the physics-based radar simulation from a "Black Box" into a transparent "Radar Lab" environment.

[!TIP] New Feature: For a detailed step-by-step guide on using these new tools, see the official Walkthrough Guide.

🎯 1. The Core Objective

To provide radar application engineers with 100% visibility into the signal processing chain. This is achieved by extracting and visualizing the internal energy states of the simulation before they are filtered into discrete points.

Key Deliverables:

  1. Visual Heatmaps: Real-time Range-Doppler (RD) and Range-Azimuth (RA) streams in Foxglove.
  2. CFAR Transparency: A visual mask of the adaptive threshold plane.
  3. Metrology Persistence: Raw .npy storage of all FFT buffers for offline validation.
  4. Signal Telemetry: JSON-based SNR and Noise Floor tracking.

🏗️ 2. Technical Architecture

The suite hooks into the ISOLATE engine at the following points:

A. Signal Generation (heatmap_gen_fast.py)

Current state: Successfully converts LiDAR points into complex ADC time-series.

  • Future Goal: Implement Multi-Path interference logic.

B. Signal Processing (radar_processor.py & cfar_detector.py)

Current state: Performs 2D FFT and CA-CFAR detection.

  • Implementation Status:
    • Exposes the 3D FFT Cube (Range x Doppler x Angle).
    • Extracts the Detection Gate from CA_CFAR.__call__.
    • Computes global Range-Azimuth energy maps via mean-Doppler reduction.

🗺️ 3. Implementation Roadmap

Phase 1: Engine Heatmap Extraction (COMPLETED)

  • Modify cfar_detector.py to return the detection_gate (Threshold Baseline).
  • Update radar_processor.py to capture RD and compute global RA heatmaps.
  • Update model_wrapper.py to expose heatmaps and signal telemetry.

Phase 2: Signal-to-Visual Pipeline (COMPLETED)

  • Implement 8-bit normalization and Viridis colormapping for radar image topics.
  • Update test_shenron.py to register new /heatmaps/ image channels in MCAP.
  • Add JSON telemetry packaging for SNR/Noise metrics.

Phase 3: Raw Data Persistence (COMPLETED)

  • Create metrology/rd, metrology/ra, and metrology/cfar directory structure.
  • Implement .npy serialization for every frame during the simulation loop.

Phase 4: Verification (COMPLETED)

  • Run Iteration 18 (250-frame simulation sweep).
  • Inspect raw .npy files for precision integrity.
  • Verify Foxglove visual alignment.

Phase 5: Pipeline Alignment (COMPLETED)

  • Integrated Multi-Radar support (awrl1432 + radarbook).
  • Standardized Topic Naming (/radar/native, /radar/{type}/metrics).
  • Synchronized run.bat logic for Dashboard-ready automation.

🧭 4. Pointers for Future Agents

  • Coordinate Frame: Always remember: Index 0 = Side (Y), Index 1 = Forward (X).
  • Normalization: When converting raw FFT magnitudes to images, use a log-dB scale to preserve dynamic range.
  • Performance: Keep the signal.convolve2d calls optimized; we must maintain at least 1.0 FPS for UX.

Created by Antigravity | Project: Fox CARLA ADAS | 2026-04-08