feat(radar): Major Update - Shenron Modularity and Physics Refinement (Iteration 18)

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.

gemini.md

@@ -24,7 +24,7 @@ This document is the consolidated source of truth for AI agents working on the F
 - `intel/`: Detailed deep-dive documentation for specific components.
 - `intel/CHRONICLES.md`: **Project History** — Weekly evolution and major milestones.
 - `intel/memory_update.md`: **Agent Protocol** — Standardized guide for repo memory updates.
-- `intel/radar/`: Deep dives into radar physics (e.g. `isotropic_illumination_problem.md`).
+- `intel/radar/`: Deep dives into radar physics (e.g. `research/isotropic_illumination_problem.md`).
 - `scripts/`: Utility scripts for data processing and **Metrology Analysis** (`track_full_state.py`, etc).
 
 ---
@@ -95,4 +95,4 @@ This document is the consolidated source of truth for AI agents working on the F
 
 ---
 
-*Generated by Antigravity AI | Last Updated: 2026-04-14 | Refer to .cursorrules for start-of-turn instructions.*
+*Generated by Antigravity AI | Last Updated: 2026-04-15 | Refer to .cursorrules for start-of-turn instructions.*

intel/CHRONICLES.md

@@ -122,7 +122,15 @@ After 30 iterations of parameter tuning, we identified the fundamental reason fo
 
 ## 📜 6. Detailed Daily Chronology (Git Absolute)
 
-### **April 14 (Today)**
+### **April 15 (Today)**
+- **Task**: Modular Architecture & Calibration Refinement.
+- **Action**: Transitioned Shenron to a fully modular "Knobs and Dials" architecture.
+- **Modularity**: Isolated radar hardware profiles (AWRL1432, TI-Cascade, Radarbook) in `ConfigureRadar.py`.
+- **Calibration**: Finalized the **Antenna Gain LUT Integration** (Separable Az/El patterns) in `Sceneset.py`.
+- **Organization**: Reorganized the `intel/radar` directory into structured subfolders (`core`, `research`, `diagnostics`, `archive`) to maintain documentation scalability.
+- **Physics**: Validated the pure physical $1/R^4$ power law and removed all legacy scaling multipliers.
+
+### **April 14**
 - **Task**: Physics "De-Hacking" & Root Cause Analysis.
 - **Action**: Identified the **Isotropic Illumination Problem** as the primary blocker for ADAS tracking. 
 - **Physics**: Stripped legacy scaling multipliers (`~277x`) and tightened reflection cones (`5.0° -> 2.0°`) from `Sceneset.py` to restore physical realism.
@@ -165,4 +173,4 @@ After 30 iterations of parameter tuning, we identified the fundamental reason fo
 
 ---
 
-*Generated by Antigravity AI | Fox CARLA ADAS Simulation | 2026-04-14*
+*Generated by Antigravity AI | Fox CARLA ADAS Simulation | 2026-04-15*

intel/internal/context.md

@@ -344,7 +344,7 @@ class MyScenario(ScenarioBase):
 
 When working on this repository, prioritize documentation based on your specific task:
 
-- **Radar Physics or Calibration:** READ `intel/radar/Shenron_debug.md` FIRST. This is the source of truth for all FMCW and material RCS milestones.
+- **Radar Physics or Calibration:** READ `intel/radar/diagnostics/Shenron_debug.md` FIRST. This is the source of truth for all FMCW and material RCS milestones.
 - **Scenario Creation:** READ `intel/internal/context.md` for the plugin contract and `intel/scenarios/braking.md` for spawning examples.
 - **Dashboard or GUI Logic:** READ `intel/scenarios/dashboard.md` for SSE and Flask-to-Subprocess architecture.
 - **Historical Context:** Check `intel/internal/old_implement.md` if the user refers to legacy "Transfuser++" patterns.

intel/memory_update.md

@@ -10,7 +10,7 @@ This document instructs future AI agents on how to maintain the repository's doc
 | :--- | :--- | :--- |
 | `intel/CHRONICLES.md` | The Project Saga | Git milestones, technical "Why" & "How," bug post-mortems. |
 | `gemini.md` | Source of Truth | Architecture changes, new components, active file rules. |
-| `intel/radar/Shenron_debug.md` | Calibration Log | Radar iterations (01-XX), specific physics fixes. |
+| `intel/radar/diagnostics/Shenron_debug.md` | Calibration Log | Radar iterations (01-XX), specific physics fixes. |
 | `intel/scenarios/*.md` | Scenario Guides | Decision rationale for specific testing maneuvers. |
 
 ---
@@ -42,4 +42,4 @@ Don't just log *what* happened. Documentation must explain:
 - **No Fluff**: Keep descriptions concise but info-dense.
 
 ---
-*Generated by Antigravity AI | Repository Memory System | 2026-04-14*
+*Generated by Antigravity AI | Repository Memory System | 2026-04-15*

intel/radar/Shenron_Debug_Plan.md → intel/radar/archive/Shenron_Debug_Plan.md

@@ -39,8 +39,8 @@ This document tracks the resolution of data-fidelity issues in the C-SHENRON rad
 ---
 
 ## 🛰️ 3. Relevant Documentation
-- [3D Vertical Energy Suppression](file:///d:/CARLA/CARLA_0.9.16/PythonAPI/Fox/intel/radar/3D%20vertical%20energy%20suppression.md)
-- [Main Debug Log (Shenron_debug.md)](file:///d:/CARLA/CARLA_0.9.16/PythonAPI/Fox/intel/radar/Shenron_debug.md)
+- [3D Vertical Energy Suppression](file:///d:/CARLA/CARLA_0.9.16/PythonAPI/Fox/intel/radar/research/3D%20vertical%20energy%20suppression.md)
+- [Main Debug Log (Shenron_debug.md)](file:///d:/CARLA/CARLA_0.9.16/PythonAPI/Fox/intel/radar/diagnostics/Shenron_debug.md)
 - [Deep Metrology Report (Iteration 17)](file:///C:/Users/rakadu1/.gemini/antigravity/brain/67913a3c-cbc2-4fba-87e3-88fbea20f043/metrology_report.md)
 
 ---

intel/radar/3D energy compression debug.md → intel/radar/diagnostics/3D energy compression debug.md

@@ -71,7 +71,7 @@ Solve the physical inconsistency where increasing LiDAR resolution increases Rad
 | **`sim_radar_utils/config.yaml`** | DSP Config | Adjust `threshold` and `guard cells` after vertical energy is balanced. |
 
 ### Knowledge Base
-- **Calibration History:** [Shenron_debug.md](file:///d:/CARLA/CARLA_0.9.16/PythonAPI/Fox/intel/radar/Shenron_debug.md) (Iterations 01-13).
+- **Calibration History:** [Shenron_debug.md](file:///d:/CARLA/CARLA_0.9.16/PythonAPI/Fox/intel/radar/diagnostics/Shenron_debug.md) (Iterations 01-13).
 - **Architecture Guide:** [context.md](file:///d:/CARLA/CARLA_0.9.16/PythonAPI/Fox/intel/internal/context.md) (AI navigation rules).
 
 ---

intel/radar/Shenron_debug.md → intel/radar/diagnostics/Shenron_debug.md

@@ -231,7 +231,7 @@ python scripts/test_shenron.py --iter "07_high_def_sync"
 | `scripts/test_shenron.py` | Testbench — generates and packages iterations |
 | `scripts/data_to_mcap.py` | Main MCAP converter (Dashboard path) |
 | `src/recorder.py` | Data capture — includes velocity + semantic metadata |
-| `intel/radar/shenron_architecture_deepdive.html` | Visual HTML architecture guide |
+| `intel/radar/archive/shenron_architecture_deepdive.html` | Visual HTML architecture guide |
 
 ---
 

scripts/ISOLATE/e2e_agent_sem_lidar2shenron_package/README.md

@@ -1,16 +1,33 @@
-# SHENRON: Radar Simulation
-Packaging shenron into minimal working code
+# SHENRON: Modular Radar Simulation Package
 
-To run the simulation, follow these steps:
+This package contains the high-fidelity, physics-based radar simulation engine isolated from the main CARLA ADAS pipeline. It uses CARLA Semantic LiDAR as the primary input and synthesizes raw ADC-like radar signals.
 
-1. Open the `simulator_config.yaml` file.
-2. Add the file paths for the input and output files in the appropriate fields. For example:
+## 🚀 Iteration 18: The "Knobs and Dials" Milestone
+The engine has been restructured for modularity. You can now tune individual radar profiles without touching the core physics logic.
 
-   ```yaml
-   PATHS :
-     LIDAR_PATH : "/home/Kshitiz/"
-     LIDAR_FOLDERS : ["semantic_lidar"]
-     OUT_PATH : "/home/Kshitiz/semantic_lidar/"
-3. Run the simulation using main file
-   ```python
+## 🏗️ Package Structure
+- `main.py`: Entry point for standalone execution.
+- `ConfigureRadar.py`: **The Control Panel.** Define hardware profiles (BW, Chirps, LUTs here).
+- `lidar.py`: Handles point cloud normalization and axis-swapping (CARLA -> Shenron).
+- `shenron/Sceneset.py`: Core physics engine (Fresnel + scattering + path loss).
+- `shenron/heatmap_gen_fast.py`: GPU-accelerated signal synthesis.
+
+## 🛠️ Usage
+
+1. **Configure Hardware:**
+   Edit `ConfigureRadar.py` to select/tune your radar profile (e.g., `awrl1432`).
+
+2. **Set Paths:**
+   Edit `carla_shenron_config.yaml` to point to your LiDAR dataset and output directories.
+
+3. **Run Simulation:**
+   ```powershell
+   # Windows (Conda environment required)
    python main.py
+   ```
+
+## 📡 Physics Consistency
+This package strictly enforces the **1/R⁴ Radar Range Equation**. All legacy scaling multipliers have been removed to ensure the simulation matches real-world hardware intensity profiles.
+
+---
+*Fox CARLA ADAS Simulation | Shenron Physics Engine | 2026-04-15*

tmp/commit_message.txt

@@ -0,0 +1,30 @@
+feat(radar): Major Update - Shenron Modularity and Physics Refinement (Iteration 18)
+
+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.