Simulator/intel/radar/archive/Azimuth_Elevation_Gain_Implementation.md

Below is a precise, implementation‑ready plan tailored to your current Shenron architecture. This plan assumes both Azimuth and Elevation LUTs already exist and focuses on minimal, safe, radar‑local integration.

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Implementation Plan: Azimuth & Elevation Radiation Gain Integration

Goal

Introduce radar‑specific azimuth and elevation antenna gains into Shenron such that:

• Each radar applies its own antenna pattern
• No shared energy values are contaminated
• Existing energy models and ADC pipelines remain intact
• Integration aligns with current physics and code structure

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1. Data Model Extensions (ConfigureRadar.py)

1.1 Add Antenna LUT Fields to Radar Configuration

Extend the radar configuration object to carry antenna patterns.

New Radar Attributes

• radar.azimuth_lut
• radar.elevation_lut
• radar.azimuth_lut_angles (degrees or radians)
• radar.elevation_lut_angles
• radar.antenna_gain_mode (e.g., "separable")

These must be radar‑instance local, not global.

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1.2 LUT Normalization Convention (Decide Once)

Choose and enforce one convention:

• Linear gain (recommended, to match existing loss math)
  • LUT values ∈ ℝ⁺
• OR
• dB gain, converted to linear on load

✅ Best practice:
Convert LUTs to linear gain at load time and store only linear values.

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2. Angle Availability Contract

2.1 Confirm Angle Variables

From your architecture:

• Azimuth (theta)
• Elevation (elev_angle) are already computed in specularpoints().

Action

• Ensure both angles are:
  • In a known unit (prefer radians internally)
  • Passed directly to get_loss_3() with no recomputation

No structural changes needed here.

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3. Loss Chain Integration Point (Sceneset.py)

3.1 Insert Antenna Gain in get_loss_3()

Modify get_loss_3() to include antenna LUT gain.

Exact placement

• After range loss
• Before material / roughness losses
• Adjacent to existing vertical antenna gain logic

This preserves physical correctness:

Antenna attenuates the incident and received wave, not the material physics.

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3.2 Compute Antenna Gain Per Point

For each point $$i$$:

1. Clamp / wrap angles to LUT domain

   • Azimuth: e.g. $$[-90°, +90°]$$
   • Elevation: e.g. $$[-45°, +45°]$$

2. Lookup gains:

   • G_az[i] = interp(radar.az_lut, theta[i])
   • G_el[i] = interp(radar.el_lut, elev_angle[i])

3. Combine gains (separable model):

$$ G_{ant}[i] = G_{az}[i] \times G_{el}[i] $$

4. Apply:

$$ loss[i] \leftarrow loss[i] \times G_{ant}[i] $$

✅ Fully vectorized NumPy operation.

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4. Multi‑Radar Safety Guarantees

No special handling required if the following rules are followed:

• Never modify shared semantic_lidar_data
• Antenna gain applied only to:
  • Local loss arrays inside get_loss_3()
• No mutation of radar‑agnostic structures

Your architecture already satisfies these conditions.

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5. Configuration Loading & Switching

5.1 Attach LUTs per Radar Profile

In ConfigureRadar.py:

• Load LUTs as part of radar initialization
• Support per‑profile antenna selection:
  • "awrl1432_front"
  • "awrl1432_corner"
  • "generic_patch"

5.2 Runtime Safety

Since each ShenronRadarModel owns its radar instance:

• Switching patterns between radars is safe
• No cross‑profile contamination possible

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6. ADC Pipeline Interaction (No Changes Required)

Because:

• Antenna gain is applied before ADC synthesis
• Noise is added later in heatmap_gen_fast.py

You automatically get:

• Angle‑dependent SNR
• Realistic CFAR behavior
• Hardware‑like edge‑of‑FOV noise dominance

✅ Do not touch ADC or noise code.

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7. Validation Hooks (Strongly Recommended)

7.1 Internal Diagnostics

Add (optional but useful):

• Store G_az, G_el, G_ant in last_metrology
• Log min / max / mean gain per frame

7.2 Visual Validation

Confirm:

• Point density decreases smoothly at FOV edges
• Peak intensity aligns at boresight
• Multiple radars show different angular falloff in the same scene

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8. Performance Impact

• Operations: O(N) per radar, vectorized
• N ≈ 5k–15k points
• LUT interpolation cost negligible

✅ Safe for real‑time simulation with multiple radars.

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9. Extension‑Ready Design

This structure naturally supports later:

• 2D az‑el LUTs
• Per‑virtual‑antenna offsets
• Beamforming‑aware gain shaping

No redesign required.

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✅ Final Summary

Aspect	Status
Radar‑specific	✅ Yes
Multi‑radar safe	✅ Yes
Minimal code changes	✅ Yes
Physically correct	✅ Yes
ADC‑correct integration	✅ Yes

This plan integrates azimuth & elevation radiation patterns into Shenron cleanly, deterministically, and without architectural risk.

If you want, next I can:

• Write exact pseudocode for get_loss_3()
• Define LUT file formats
• Suggest default az/el cutoffs for AWRL1432
• Design a radar antenna profile schema

Just say what’s next.