High-Fidelity Deterministic
Radar Simulation

Moving from bounding-box emulation to mathematically pure, hardware-configurable physics validating the Shenron Pipeline.

The Physical Foundation: 1/R⁴ Physics

Standard simulator bounding-boxes bypass the complex realities of electromagnetic propagation. The Shenron engine enforces strict free-space physics for absolute accuracy.

Milestone R4: Pure Physics

We abandoned artificial density and distance normalizations. All generated scatterers now strictly follow the Radar Range Equation (1/R⁴). A building at distance naturally drops in power compared to a vehicle up close.

Deterministic Coordinate Control

Standard Traffic Manager leads to high variance. Using a Spawn-and-Move logic with enforced Velocity overrides guarantees complex scenarios (like left-turn cut-ins) occur at the exact same millisecond every run.

Insert Screenshot: 1/R⁴ Contrast
Provide a side-by-side or CFAR output showing how distant heavy clutter is naturally attenuated while the closer vehicle is correctly highlighted due to the inverse square law path loss.

Unprecedented Configurability: "Knobs & Dials"

The Shenron architecture eliminates hardcoded physical scalars. Instead, it exposes a "Control Panel" mimicking real hardware tuning. If we do it on the test track, we can do it in the simulation.

Supported Hardware Profiles

TI AWRL1432 (ADAS) Radarbook (Research) TI Cascade (MIMO)
  • Bandwidth (B) & Chirps: Accurately adjust range and doppler resolution directly reflecting radar specifications.
  • System Gain Calibration (110dB): Modify the global signal chain gain ($P_t G_t G_r$) without touching the core physics engine.
  • Antenna Arrays (nRx/nTx): Instantly swap between 6 virtual receivers (1432) or 8 virtual receivers (Radarbook) to measure DSP beamforming limits.
  • Noise Amplitude: Dynamically inject AWGN thermal noise to test CFAR floor robustness.
Insert Code/UI Screenshot: Customizing Radar
Please insert a screenshot showing `ConfigureRadar.py` or the specific JSON payload being passed via the dashboard to demonstrate how easily we swap between a 1432 profile and a Radarbook profile.

Deep Observability: The Metrology Suite

Instead of treating synthetic radar output as a "black box," our new pipeline visualizes the entire signal chain. This allows engineers to cross-validate ADC output against physical ground truth.

The "Radar Blue" Standard

We instituted phase-preserving Doppler-Slice Synthesis to replace artifact-heavy coherent integration, resulting in pristine, sharp heatmaps.

  • Range-Doppler (RD): Extracts target separation and velocity, preventing velocity zeroing.
  • Range-Azimuth (RA): Confirms azimuthal accuracy utilizing Zero-Tilt Symmetry (120° FOV perfectly aligned to boresight).
  • CFAR Gate: Visualizes adaptive thresholds distinguishing targets from the synthesized noise floor.
Insert Screenshot: 3-Plot Metrology Suite
Insert a screenshot of the Foxglove layout showing the 3 core metrology graphs (RD, RA, and CFAR) running alongside the 3D Pointcloud view to illustrate deep observability.

Major Breakthroughs & Bug Squashing Engine

Achieving this level of physical simulation required identifying and tearing down several legacy miscalculations that corrupted early models.

The Isotropic Illumination Fix

The deepest physics problem resolved. Early simulation allowed side-targets at 80° to return 100% power, masking forward vehicles.

We implemented Horizontal (Azimuth) & Vertical (Elevation) Directional Gain Patterns. Clutter at wide angles is now correctly attenuated by ~15-20 dB, letting the true target dominate.

The Cos(Cos) Reflection Error

Recovered 46% of lost signal energy by tracking down a double-cosine application in the Open3D coordinate translation pipeline.

Floating Point Data Packing

Resolved the infamous "0.0 velocity" bug by utilizing `np.view(np.uint32)` to correctly deserialize perfectly packed 32-bit semantic tags.

The Full Automation Pipeline

A completely automated workflow enabling one-click testbenches, fully orchestrated via a web dashboard.

Insert Graphic/Screenshot: Pipeline & Dashboard
Place a diagram or screenshot here showing the flow:

1. GUI Dashboard (with Hardware GPU Idle Mode) → 2. CARLA Simulator → 3. Shenron Physics → 4. MCAP Serializer → 5. Foxglove Studio

From deterministic scenario configuration to a 3D Foxglove validation pointcloud — without manual intervention.