This release marks a major architectural shift and achieves full feature
parity with the high-fidelity Shenron radar testbench.
Architecture:
- Implemented PipelineManager and Stage-based execution (Sim, Shenron, MCAP, Video).
- Decoupled recorder from physics/augmentation logic into src/processing/physics.py.
- Migrated all path resolution to Pathlib for cross-platform robustness.
Radar & Metrology:
- Integrated FastHeatmapEngine for stateful, high-performance RD/RA/CFAR rendering.
- Implemented dual-plot Range-Azimuth (Static Absolute vs Dynamic Peak).
- Flattened telemetry schema for direct Foxglove Plot panel compatibility.
- Added hardware-accurate 3D FOV frustum projections (LINE_LIST).
Dashboard & UX:
- Bumped version to v1.1.
- Fixed stop.flag detection bug in simulation shutdown.
- Optimized GPU headroom by maintaining idle/sync mode during synthesis.
Signed-off-by: Antigravity AI <cortex@deepmind.google>
Transitioned the Shenron radar engine from a rigid architecture to a modular,
tunable "Knobs and Dials" framework. This update establishes a physics-first
baseline derived from real-world electromagnetic behavior.
Core Changes:
- Modular Radar Profiles: Pre-configured profiles for TI Cascade, Radarbook,
and AWRL1432 with hardware-specific Bandwidth, Chirp, and nRx parameters.
- Physics Core (Sceneset.py):
- Full implementation of Fresnel and Beckmann-Spizzichino scattering.
- Pure 1/R^4 power law (1/R^2 transmit, 1/R^2 receive) via legacy scaling removal.
- Fixed cos(cos(theta)) bug in CARLA semantic lidar mapping.
- Antenna Gain Integration:
- Implemented separable Azimuth/Elevation gain patterns.
- Added Symmetric Azimuth LUT interpolation and Vertical FOV Hard Cutoff.
- Signal Processing:
- Optimized GPU-accelerated signal synthesis using PyTorch.
- Standardized 110 dB System Calibration Constant for hardware SNR matching.
Architecture & Documentation (intel/radar):
- Reorganized radar intel directory into structured subfolders (core, research,
diagnostics, archive) for better scalability.
- Added SHENRON_MODULAR_ARCHITECTURE.md (System overview).
- Added SHENRON_ANTENNA_GAIN_CALIBRATION.md (Gain physics deep-dive).
- Modernized package README with Windows/Conda specific usage instructions.
- Synchronized all internal and external documentation links.
Calibration: Iteration 18 (Antenna Gain Baseline)
Note: Remaining magic numbers in get_loss_3 are noted for future migration.
This commit marks the completion of the Iteration 13 'Golden Mix' calibration.
- intel/: Structured documentation into /radar, /scenarios, and /internal subfolders.
- Shenron_debug.md: Comprehensive history of all 13 calibration iterations.
- radar_processor.py: Implemented Blackman-Harris windowing for sidelobe suppression.
- lidar.py: Applied -2.0m parallax shift and ground suppression filtering.
- Sceneset.py: Calibrated specular thresholds and material roughness for stability.
RUSHIL AMBARISH KADU