- Expanded intel/internal/context.md with modern stage-based architecture and internal documentation structure.
- Corrected SNR calculation in model_wrapper.py by dividing out the CFAR threshold.
- Tuned CFAR threshold to 19dB for increased point cloud density.
- Integrated stop flag detection into PipelineManager and ShenronOrchestrator for graceful halting.
- Synchronized metrology gain offsets (-78.0dB) across production and testbench scripts.
- Added 'ti_cascade' support to default radar list and testbench visualization.
Established a common orchestration framework to eliminate logic
duplication between production data generation and the iterative
testbench.
Key changes:
- Created 'ShenronOrchestrator' in scripts/ISOLATE/shenron_orchestrator.py
to serve as the single source of truth for the processing loop.
- Refactored 'generate_shenron.py' to use the orchestrator, ensuring
production data benefit from research-level DSP improvements.
- Refactored 'test_shenron.py' to use the orchestrator, guaranteeing
that debug iterations are bit-identical to production outputs.
- Centralized LiDAR padding, model execution, and metrology/metric
serialization logic.
- Preserved Dashboard SSE telemetry patterns ([SHENRON_INIT/STEP])
to maintain full UI compatibility.
This restructuring ensures that any iterative changes made in the
'test_shenron' lab are automatically and safely inherited by the
Dashboard's automated simulation pipeline.
- [Physics] Disable R^2 area expansion in Sceneset.get_loss_3 to restore
physically correct 1/R^4 two-way radar power law (point scatterer model)
- [Metrology] Normalize RA heatmap [0,1] before variance calculation in
model_wrapper.py to prevent 1e20 scalar overflow values
- [Metrology] Add peak_azimuth_spread_deg (Half-Power Beamwidth proxy)
to signal metrics for per-frame angular resolution monitoring
- [DSP] Switch spatial Angle-FFT window from Hanning to Hamming in
radar_processor.py for sharper beamforming on small antenna arrays (6-8 Rx)
- [Telemetry] Expose az_std and spread in [SHENRON_STEP] dashboard stream
for both generate_shenron.py and test_shenron.py pipelines
- [BugFix] Add missing sys/os imports in src/main.py and video_stage.py
that caused NameError crashes in the stage-based pipeline orchestrator
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>
Restores high-fidelity physical realism to the C-Shenron radar engine by
aligning the synthesis pipeline with the pure Radar Range Equation.
Core Improvements:
- Physics: Restored 1/R^4 power-delay law (1/R^2 voltage) in Sceneset.py
and heatmap_gen_fast.py. Stripped legacy 1/1000 normalizations and
R^2 area-growth workarounds.
- Geometry: Fixed FFT index asymmetry in radar_processor.py, achieving a
perfectly symmetric 120° FOV sector.
- Metrology: Implemented "Radar Blue" (Viridis) 120° fan-projection for
diagnostic Range-Azimuth heatmaps.
- Automation: Integrated RD/RA/CFAR heatmap persistence into the
automated simulation-to-MCAP pipeline (data_to_mcap.py).
- Docs: Comprehensive update of intel/ directory, including Iterations 17-26
and the Physics/Symmetry Milestone deep-dive.
This milestone ensures that target brightness and spatial positioning
correctly mimic real-world TI AWRL1432 radar hardware.
Comprehensive integration of the Shenron radar model, enabling high-fidelity
physics-driven radar synthesis from semantic LiDAR data.
Core Changes:
- Sensor Migration: Updated EGO vehicle to use 'sensor.lidar.ray_cast_semantic'.
- Recorder Enhancement: Modified src/recorder.py to dynamically capture
6/7nd-column semantic LiDAR buffers [x, y, z, cos, obj, tag].
- Orchestration: Created scripts/ISOLATE/model_wrapper.py with the
ShenronRadarModel class for a unified physics-to-detection API.
- Processor Logic: Fixed broadcasting bugs and hardcoded antenna counts
in RadarProcessor to support dynamic radar models (radarbook/ti_cascade).
- Rich Data Output: Updated extraction logic to include [velocity, magnitude]
parameters for ADAS performance analysis.
- Batch Pipeline: Added scripts/generate_shenron.py to synthesize radar
data as a post-processing step, maintaining simulation performance.
- Visualization: Updated scripts/data_to_mcap.py to support the
new /radar/shenron topic with a 5-field rich PointCloud schema.
Stability & Fixes:
- Implemented CUDA-enabled PyTorch checks with a robust CPU fallback.
- Corrected CARLA 0.9.16 semantic column mapping in ISOLATE/lidar.py.
- Handled indexing and range-slicing errors in the CFAR detection routine.
RUSHIL AMBARISH KADU