#!/usr/bin/env python3
/github/bgilbert1984/NerfEngine/RF_QUANTUM_SCYTHE/SignalIntelligence/real_world_integration.md
# RF Quantum SCYTHE Triangulation Integration Guide
This document provides guidance on integrating the enhanced triangulation modules into a real-world RF signal intelligence workflow as part of the RF Quantum SCYTHE system.
## Table of Contents
1. [Overview](#overview)
2. [Module Architecture](#module-architecture)
3. [Integration Points](#integration-points)
4. [Pipeline Configuration](#pipeline-configuration)
5. [Calibration Procedures](#calibration-procedures)
6. [Performance Considerations](#performance-considerations)
7. [Deployment Checklist](#deployment-checklist)
## Overview
The enhanced triangulation system provides several key capabilities:
– **Enhanced Soft Triangulator**: Robust angle-only triangulation with confidence weighting and uncertainty estimation
– **Hybrid Triangulator**: Combined AoA and TDoA triangulation with robust outlier rejection
– **Triangulation Loss**: End-to-end trainable loss function for physics-informed learning
These modules can be integrated at multiple points in the RF SCYTHE pipeline to enhance geolocation capabilities.
## Module Architecture
“`
├── soft_triangulator_enhanced.py # Core triangulation functionality
│ ├── EnhancedSoftTriangulator # Improved AoA triangulation
│ └── HybridTriangulator # Combined AoA + TDoA triangulation
│
├── triangulation_loss.py # End-to-end training components
│ ├── TDoALossConfig # Configuration for training
│ └── TriangulationLoss # Physics-informed loss function
│
├── demo_hybrid_triangulator.py # Demonstration and visualization
└── rf_beamforming_tdoa_train.py # Example training pipeline
“`
## Integration Points
### 1. Real-time Signal Processing Pipeline
Integrate the triangulation modules into the real-time signal processing pipeline:
“`python
from soft_triangulator_enhanced import EnhancedSoftTriangulator, HybridTriangulator
# Initialize once
triangulator = HybridTriangulator(
angle_bins=your_angle_bins, # Must match beam pattern configuration
max_range=your_max_range, # Maximum expected range in meters
num_refine_steps=10, # Higher for accuracy, lower for speed
refine_lr=1.0, # Learning rate for gradient refinement
huber_delta=3.0 # Robustness parameter (higher = more forgiving)
)
# In processing loop
def process_signal_detection(beam_logits, sensor_positions, tdoa_measurements=None):
“””Process incoming signal detection for geolocation”””
# Format TDoA measurements if available
tdoa_pairs = None
if tdoa_measurements is not None:
tdoa_pairs = {
“i”: tdoa_measurements.sensor_i_indices,
“j”: tdoa_measurements.sensor_j_indices,
“tdoa_s”: tdoa_measurements.time_differences,
“sigma_s”: tdoa_measurements.uncertainties,
“w”: tdoa_measurements.confidence_weights
}
# Run triangulation
result = triangulator(beam_logits, sensor_positions, tdoa_pairs=tdoa_pairs)
# Extract results
estimated_position = result[“pos_xy”]
uncertainty_params = result[“uncertainty”] # [major_axis, minor_axis, angle_rad]
# Calculate confidence metrics
is_high_confidence = (uncertainty_params[:, 0] < 500).float()
return {
“position”: estimated_position,
“uncertainty”: uncertainty_params,
“confidence”: is_high_confidence,
“initial_position”: result.get(“initial_pos_xy”, None),
“tdoa_residual”: result.get(“tdoa_residual”, None)
}
“`
### 2. Model Training Pipeline
For training RF beamforming networks with physics-informed losses:
“`python
from triangulation_loss import TriangulationLoss, TDoALossConfig
# Configure loss
loss_config = TDoALossConfig(
position_weight=1.0, # Weight for direct position supervision
tdoa_weight=1.0, # Weight for TDoA consistency
angle_weight=0.1, # Weight for angle consistency
entropy_weight=0.01, # Entropy regularization
num_refine_steps=5 # TDoA refinement steps (0 to disable)
)
# Create loss module
triangulation_loss = TriangulationLoss(angle_bins, loss_config)
# In training loop
def training_step(batch):
# Forward pass
outputs = model(batch[‘features’])
beam_logits = outputs[‘beam_logits’]
# Apply triangulation loss
loss_dict = triangulation_loss(
beam_logits=beam_logits,
sensor_xy=batch[‘sensor_positions’],
target_positions=batch.get(‘true_positions’), # Optional
tdoa_pairs=batch.get(‘tdoa_measurements’) # Optional
)
# Extract losses
total_loss = loss_dict[‘loss’]
# Backward pass
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
return loss_dict
“`
## Pipeline Configuration
### Configuration Parameters
The triangulation system has several important configuration parameters:
| Parameter | Description | Typical Value | Notes |
|———–|————-|————–|——-|
| `angle_bins` | Angle bin centers | linspace(-90°, 90°, 181) | Must match beam pattern |
| `max_range` | Maximum range | 5000.0 m | Adjust for your deployment |
| `temp` | Softmax temperature | 1.0 | Higher = smoother probability |
| `robust_threshold` | Outlier rejection | 3.0 | Higher = more inclusive |
| `num_refine_steps` | TDoA refinement | 10 | More steps = higher accuracy |
### Real-world Considerations
For optimal performance in real-world scenarios:
1. **Beam pattern configuration**: Ensure the angle bins match the physical configuration of your sensors
2. **Sensor position accuracy**: Precisely measure and calibrate your sensor positions
3. **Clock synchronization**: For TDoA, ensure sensors have synchronized clocks or measure/model clock bias
4. **Interference handling**: Use the robust outlier rejection to handle interference and multipath
5. **Mobile sensors**: Update sensor positions in real-time for mobile platforms
## Calibration Procedures
Before deploying in production, perform these calibration procedures:
### 1. Angle Calibration
1. Place a known emitter at a fixed position
2. Record beam patterns from each sensor
3. Calculate the angular offset between expected and measured angles
4. Apply offset corrections to the beam patterns
### 2. TDoA Calibration
1. Measure and record clock biases between sensors
2. Place a known emitter at multiple positions
3. Record TDoA measurements
4. Calculate calibration parameters to minimize TDoA error
### 3. System Verification
1. Deploy known emitters at various positions across the operational area
2. Verify triangulation accuracy at each position
3. Adjust uncertainty models based on empirical error distribution
4. Validate the confidence metrics against ground truth accuracy
## Performance Considerations
### Computational Performance
The triangulation system has been optimized for real-time operation:
– **AoA-only triangulation**: Fast and suitable for high-throughput scenarios
– **Hybrid triangulation**: More computationally intensive but more accurate
– **Batch processing**: All operations support batch processing for efficiency
For resource-constrained environments:
– Reduce `num_refine_steps` to decrease computation time
– Use `EnhancedSoftTriangulator` instead of `HybridTriangulator`
– Pre-compute sensor pair matrices for frequently used sensor configurations
### Accuracy Considerations
Factors affecting triangulation accuracy:
1. **Beam pattern resolution**: More angle bins can improve precision
2. **Sensor geometry**: Wider distribution of sensors improves triangulation
3. **Signal strength**: Stronger signals typically yield better beam estimates
4. **Multipath effects**: Use robust triangulation to mitigate multipath issues
5. **Number of sensors**: More sensors provide redundancy and improved accuracy
## Deployment Checklist
Before deploying to production:
– [ ] Configure angle bins to match physical sensor capabilities
– [ ] Calibrate sensor positions and orientations
– [ ] Measure and apply clock bias corrections
– [ ] Test with known emitter positions across the operational area
– [ ] Validate uncertainty estimates against empirical error distributions
– [ ] Set appropriate thresholds for confidence metrics
– [ ] Integrate with downstream tracking and classification systems
– [ ] Set up monitoring for triangulation quality metrics
## Advanced Integration Example
Here’s a more advanced integration example showing how to combine the triangulation with tracking and classification:
“`python
# Import modules
from soft_triangulator_enhanced import HybridTriangulator
from rf_sequence_recovery_tdoa import TrajectoryInferrer
# Initialize components
triangulator = HybridTriangulator(angle_bins, max_range=5000.0)
trajectory_inferrer = TrajectoryInferrer()
# Process a batch of detections
def process_detection_batch(detections):
# Group by detection ID/time
detection_groups = group_detections_by_time(detections)
# Process each group
results = []
for detection_group in detection_groups:
# Extract beam logits and sensor positions
beam_logits = detection_group.beam_logits
sensor_xy = detection_group.sensor_positions
tdoa_data = detection_group.tdoa_measurements
# Format TDoA pairs
tdoa_pairs = format_tdoa_pairs(tdoa_data)
# Run triangulation
tri_result = triangulator(beam_logits, sensor_xy, tdoa_pairs=tdoa_pairs)
# Add to trajectory tracking
trajectory_inferrer.add_sighting(
timestamp=detection_group.timestamp,
position=tri_result[“pos_xy”],
uncertainty=tri_result[“uncertainty”],
tdoa_residual=tri_result.get(“tdoa_residual”, None)
)
# Store results
results.append({
“timestamp”: detection_group.timestamp,
“position”: tri_result[“pos_xy”],
“uncertainty”: tri_result[“uncertainty”]
})
# Estimate trajectory
trajectory = trajectory_inferrer.infer_trajectory()
return {
“detections”: results,
“trajectory”: trajectory
}
“`
This integration approach enables seamless transition from individual position estimates to trajectory inference for moving emitters.