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.

🎯 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:

Visual Heatmaps: Real-time Range-Doppler (RD) and Range-Azimuth (RA) streams in Foxglove.
CFAR Transparency: A visual mask of the adaptive threshold plane.
Metrology Persistence: Raw .npy storage of all FFT buffers for offline validation.
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.

Modification Plan:
- Retain the 3D FFT Cube (Range x Doppler x Angle).
- Extract the Threshold Matrix from CA_CFAR.__call__.
- Compute global Range-Azimuth energy maps via mean-Doppler reduction.

🗺️ 3. Active Implementation Roadmap

Phase 1: Engine Heatmap Extraction (IN PROGRESS)

Modify cfar_detector.py to return the rd_avg_noise_power (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 (PENDING)

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 (PENDING)

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

🧭 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 | 2026-04-07

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