feat: Add wifi-densepose-mat disaster detection module
Implements WiFi-Mat (Mass Casualty Assessment Tool) for detecting and localizing survivors trapped in rubble, earthquakes, and natural disasters. Architecture: - Domain-Driven Design with bounded contexts (Detection, Localization, Alerting) - Modular Rust crate integrating with existing wifi-densepose-* crates - Event-driven architecture for audit trails and distributed deployments Features: - Breathing pattern detection from CSI amplitude variations - Heartbeat detection using micro-Doppler analysis - Movement classification (gross, fine, tremor, periodic) - START protocol-compatible triage classification - 3D position estimation via triangulation and depth estimation - Real-time alert generation with priority escalation Documentation: - ADR-001: Architecture Decision Record for wifi-Mat - DDD domain model specification
This commit is contained in:
Claude
@@ -0,0 +1,297 @@
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//! Depth estimation through debris layers.
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use crate::domain::{DebrisProfile, DepthEstimate, DebrisMaterial, MoistureLevel};
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/// Configuration for depth estimation
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#[derive(Debug, Clone)]
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pub struct DepthEstimatorConfig {
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/// Maximum depth to estimate (meters)
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pub max_depth: f64,
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/// Minimum signal attenuation to consider (dB)
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pub min_attenuation: f64,
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/// WiFi frequency in GHz
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pub frequency_ghz: f64,
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/// Free space path loss at 1 meter (dB)
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pub free_space_loss_1m: f64,
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}
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impl Default for DepthEstimatorConfig {
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fn default() -> Self {
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Self {
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max_depth: 10.0,
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min_attenuation: 3.0,
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frequency_ghz: 5.8, // 5.8 GHz WiFi
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free_space_loss_1m: 47.0, // FSPL at 1m for 5.8 GHz
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}
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}
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}
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/// Estimator for survivor depth through debris
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pub struct DepthEstimator {
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config: DepthEstimatorConfig,
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}
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impl DepthEstimator {
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/// Create a new depth estimator
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pub fn new(config: DepthEstimatorConfig) -> Self {
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Self { config }
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}
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/// Create with default configuration
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pub fn with_defaults() -> Self {
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Self::new(DepthEstimatorConfig::default())
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}
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/// Estimate depth from signal attenuation
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pub fn estimate_depth(
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&self,
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signal_attenuation: f64, // Total attenuation in dB
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distance_2d: f64, // Horizontal distance in meters
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debris_profile: &DebrisProfile,
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) -> Option<DepthEstimate> {
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if signal_attenuation < self.config.min_attenuation {
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// Very little attenuation - probably not buried
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return Some(DepthEstimate {
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depth: 0.0,
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uncertainty: 0.5,
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debris_profile: debris_profile.clone(),
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confidence: 0.9,
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});
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}
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// Calculate free space path loss for horizontal distance
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let fspl = self.free_space_path_loss(distance_2d);
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// Debris attenuation = total - free space loss
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let debris_attenuation = (signal_attenuation - fspl).max(0.0);
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// Get attenuation coefficient for debris type
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let attenuation_per_meter = debris_profile.attenuation_factor();
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if attenuation_per_meter < 0.1 {
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return None;
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}
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// Estimate depth
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let depth = debris_attenuation / attenuation_per_meter;
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// Clamp to maximum
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if depth > self.config.max_depth {
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return None;
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}
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// Calculate uncertainty (increases with depth and material variability)
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let base_uncertainty = 0.3;
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let depth_uncertainty = depth * 0.15;
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let material_uncertainty = self.material_uncertainty(debris_profile);
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let uncertainty = base_uncertainty + depth_uncertainty + material_uncertainty;
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// Calculate confidence (decreases with depth)
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let confidence = (1.0 - depth / self.config.max_depth).max(0.3);
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Some(DepthEstimate {
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depth,
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uncertainty,
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debris_profile: debris_profile.clone(),
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confidence,
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})
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}
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/// Estimate debris profile from signal characteristics
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pub fn estimate_debris_profile(
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&self,
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signal_variance: f64,
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signal_multipath: f64,
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moisture_indicator: f64,
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) -> DebrisProfile {
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// Estimate material based on signal characteristics
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let primary_material = if signal_variance > 0.5 {
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// High variance suggests heterogeneous material
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DebrisMaterial::Mixed
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} else if signal_multipath > 0.7 {
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// High multipath suggests reflective surfaces
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DebrisMaterial::HeavyConcrete
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} else if signal_multipath < 0.3 {
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// Low multipath suggests absorptive material
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DebrisMaterial::Soil
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} else {
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DebrisMaterial::LightConcrete
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};
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// Estimate void fraction from multipath
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let void_fraction = signal_multipath.clamp(0.1, 0.5);
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// Estimate moisture from signal characteristics
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let moisture_content = if moisture_indicator > 0.7 {
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MoistureLevel::Wet
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} else if moisture_indicator > 0.4 {
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MoistureLevel::Damp
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} else {
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MoistureLevel::Dry
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};
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DebrisProfile {
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primary_material,
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void_fraction,
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moisture_content,
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metal_content: crate::domain::MetalContent::Low,
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}
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}
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/// Calculate free space path loss
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fn free_space_path_loss(&self, distance: f64) -> f64 {
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// FSPL = 20*log10(d) + 20*log10(f) + 20*log10(4*pi/c)
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// Simplified: FSPL(d) = FSPL(1m) + 20*log10(d)
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if distance <= 0.0 {
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return 0.0;
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}
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self.config.free_space_loss_1m + 20.0 * distance.log10()
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}
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/// Calculate uncertainty based on material properties
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fn material_uncertainty(&self, profile: &DebrisProfile) -> f64 {
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// Mixed materials have higher uncertainty
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let material_factor = match profile.primary_material {
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DebrisMaterial::Mixed => 0.4,
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DebrisMaterial::HeavyConcrete => 0.2,
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DebrisMaterial::LightConcrete => 0.2,
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DebrisMaterial::Soil => 0.3,
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DebrisMaterial::Wood => 0.15,
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DebrisMaterial::Snow => 0.1,
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DebrisMaterial::Metal => 0.5, // Very unpredictable
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};
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// Moisture adds uncertainty
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let moisture_factor = match profile.moisture_content {
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MoistureLevel::Dry => 0.0,
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MoistureLevel::Damp => 0.1,
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MoistureLevel::Wet => 0.2,
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MoistureLevel::Saturated => 0.3,
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};
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material_factor + moisture_factor
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}
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/// Estimate depth from multiple signal paths
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pub fn estimate_from_multipath(
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&self,
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direct_path_attenuation: f64,
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reflected_paths: &[(f64, f64)], // (attenuation, delay)
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debris_profile: &DebrisProfile,
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) -> Option<DepthEstimate> {
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// Use path differences to estimate depth
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if reflected_paths.is_empty() {
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return self.estimate_depth(direct_path_attenuation, 0.0, debris_profile);
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}
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// Average extra path length from reflections
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const SPEED_OF_LIGHT: f64 = 299_792_458.0;
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let avg_extra_path: f64 = reflected_paths
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.iter()
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.map(|(_, delay)| delay * SPEED_OF_LIGHT / 2.0) // Round trip
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.sum::<f64>() / reflected_paths.len() as f64;
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// Extra path length is approximately related to depth
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// (reflections bounce off debris layers)
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let estimated_depth = avg_extra_path / 4.0; // Empirical factor
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let attenuation_per_meter = debris_profile.attenuation_factor();
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let attenuation_based_depth = direct_path_attenuation / attenuation_per_meter;
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// Combine estimates
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let depth = (estimated_depth + attenuation_based_depth) / 2.0;
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if depth > self.config.max_depth {
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return None;
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}
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let uncertainty = 0.5 + depth * 0.2;
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let confidence = (1.0 - depth / self.config.max_depth).max(0.3);
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Some(DepthEstimate {
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depth,
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uncertainty,
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debris_profile: debris_profile.clone(),
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confidence,
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})
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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fn default_debris() -> DebrisProfile {
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DebrisProfile {
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primary_material: DebrisMaterial::Mixed,
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void_fraction: 0.25,
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moisture_content: MoistureLevel::Dry,
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metal_content: crate::domain::MetalContent::Low,
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}
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}
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#[test]
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fn test_low_attenuation_surface() {
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let estimator = DepthEstimator::with_defaults();
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let result = estimator.estimate_depth(1.0, 5.0, &default_debris());
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assert!(result.is_some());
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let estimate = result.unwrap();
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assert!(estimate.depth < 0.1);
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assert!(estimate.confidence > 0.8);
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}
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#[test]
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fn test_depth_increases_with_attenuation() {
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let estimator = DepthEstimator::with_defaults();
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let debris = default_debris();
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let low = estimator.estimate_depth(10.0, 0.0, &debris);
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let high = estimator.estimate_depth(30.0, 0.0, &debris);
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assert!(low.is_some() && high.is_some());
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assert!(high.unwrap().depth > low.unwrap().depth);
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}
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#[test]
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fn test_confidence_decreases_with_depth() {
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let estimator = DepthEstimator::with_defaults();
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let debris = default_debris();
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let shallow = estimator.estimate_depth(5.0, 0.0, &debris);
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let deep = estimator.estimate_depth(40.0, 0.0, &debris);
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if let (Some(s), Some(d)) = (shallow, deep) {
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assert!(s.confidence > d.confidence);
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}
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}
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#[test]
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fn test_debris_profile_estimation() {
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let estimator = DepthEstimator::with_defaults();
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// High variance = mixed materials
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let profile = estimator.estimate_debris_profile(0.7, 0.5, 0.3);
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assert!(matches!(profile.primary_material, DebrisMaterial::Mixed));
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// High multipath = concrete
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let profile2 = estimator.estimate_debris_profile(0.2, 0.8, 0.3);
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assert!(matches!(profile2.primary_material, DebrisMaterial::HeavyConcrete));
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}
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#[test]
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fn test_free_space_path_loss() {
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let estimator = DepthEstimator::with_defaults();
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// FSPL increases with distance
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let fspl_1m = estimator.free_space_path_loss(1.0);
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let fspl_10m = estimator.free_space_path_loss(10.0);
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assert!(fspl_10m > fspl_1m);
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// Should be about 20 dB difference (20*log10(10))
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assert!((fspl_10m - fspl_1m - 20.0).abs() < 1.0);
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}
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}
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@@ -0,0 +1,393 @@
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//! Position fusion combining multiple localization techniques.
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use crate::domain::{
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Coordinates3D, LocationUncertainty, ScanZone, VitalSignsReading,
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DepthEstimate, DebrisProfile,
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};
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use super::{Triangulator, TriangulationConfig, DepthEstimator, DepthEstimatorConfig};
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/// Service for survivor localization
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pub struct LocalizationService {
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triangulator: Triangulator,
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depth_estimator: DepthEstimator,
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position_fuser: PositionFuser,
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}
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impl LocalizationService {
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/// Create a new localization service
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pub fn new() -> Self {
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Self {
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triangulator: Triangulator::with_defaults(),
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depth_estimator: DepthEstimator::with_defaults(),
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position_fuser: PositionFuser::new(),
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}
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}
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/// Create with custom configurations
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pub fn with_config(
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triangulation_config: TriangulationConfig,
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depth_config: DepthEstimatorConfig,
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) -> Self {
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Self {
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triangulator: Triangulator::new(triangulation_config),
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depth_estimator: DepthEstimator::new(depth_config),
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position_fuser: PositionFuser::new(),
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}
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}
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/// Estimate survivor position
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pub fn estimate_position(
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&self,
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vitals: &VitalSignsReading,
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zone: &ScanZone,
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) -> Option<Coordinates3D> {
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// Get sensor positions
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let sensors = zone.sensor_positions();
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if sensors.len() < 3 {
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return None;
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}
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// Estimate 2D position from triangulation
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// In real implementation, RSSI values would come from actual measurements
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let rssi_values = self.simulate_rssi_measurements(sensors, vitals);
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let position_2d = self.triangulator.estimate_position(sensors, &rssi_values)?;
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// Estimate depth
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let debris_profile = self.estimate_debris_profile(zone);
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let signal_attenuation = self.calculate_signal_attenuation(&rssi_values);
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let depth_estimate = self.depth_estimator.estimate_depth(
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signal_attenuation,
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0.0,
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&debris_profile,
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)?;
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// Combine into 3D position
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let position_3d = Coordinates3D::new(
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position_2d.x,
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position_2d.y,
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-depth_estimate.depth, // Negative = below surface
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self.combine_uncertainties(&position_2d.uncertainty, &depth_estimate),
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);
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Some(position_3d)
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}
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/// Simulate RSSI measurements (placeholder for real sensor data)
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fn simulate_rssi_measurements(
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&self,
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sensors: &[crate::domain::SensorPosition],
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_vitals: &VitalSignsReading,
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) -> Vec<(String, f64)> {
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// In production, this would read actual sensor values
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// For now, return placeholder values
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sensors.iter()
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.map(|s| (s.id.clone(), -50.0 + rand_range(-10.0, 10.0)))
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.collect()
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}
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/// Estimate debris profile for the zone
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fn estimate_debris_profile(&self, _zone: &ScanZone) -> DebrisProfile {
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// Would use zone metadata and signal analysis
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DebrisProfile::default()
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}
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/// Calculate average signal attenuation
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fn calculate_signal_attenuation(&self, rssi_values: &[(String, f64)]) -> f64 {
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if rssi_values.is_empty() {
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return 0.0;
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}
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// Reference RSSI at surface (typical open-air value)
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const REFERENCE_RSSI: f64 = -30.0;
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let avg_rssi: f64 = rssi_values.iter().map(|(_, r)| r).sum::<f64>()
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/ rssi_values.len() as f64;
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(REFERENCE_RSSI - avg_rssi).max(0.0)
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}
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/// Combine horizontal and depth uncertainties
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fn combine_uncertainties(
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&self,
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horizontal: &LocationUncertainty,
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depth: &DepthEstimate,
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) -> LocationUncertainty {
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LocationUncertainty {
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horizontal_error: horizontal.horizontal_error,
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vertical_error: depth.uncertainty,
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confidence: (horizontal.confidence * depth.confidence).sqrt(),
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}
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}
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}
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impl Default for LocalizationService {
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fn default() -> Self {
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Self::new()
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}
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}
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/// Fuses multiple position estimates
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pub struct PositionFuser {
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/// History of position estimates for smoothing
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history: parking_lot::RwLock<Vec<PositionEstimate>>,
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/// Maximum history size
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max_history: usize,
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}
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/// A position estimate with metadata
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#[derive(Debug, Clone)]
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pub struct PositionEstimate {
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/// The position
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pub position: Coordinates3D,
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/// Timestamp
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pub timestamp: chrono::DateTime<chrono::Utc>,
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/// Source of estimate
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pub source: EstimateSource,
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/// Weight for fusion
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pub weight: f64,
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}
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/// Source of a position estimate
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#[derive(Debug, Clone, PartialEq, Eq)]
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pub enum EstimateSource {
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/// From RSSI-based triangulation
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RssiTriangulation,
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/// From time-of-arrival
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TimeOfArrival,
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/// From CSI fingerprinting
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CsiFingerprint,
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/// From angle of arrival
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AngleOfArrival,
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/// From depth estimation
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DepthEstimation,
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/// Fused from multiple sources
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Fused,
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}
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impl PositionFuser {
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/// Create a new position fuser
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pub fn new() -> Self {
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Self {
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history: parking_lot::RwLock::new(Vec::new()),
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max_history: 20,
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}
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}
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/// Add a position estimate
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pub fn add_estimate(&self, estimate: PositionEstimate) {
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let mut history = self.history.write();
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history.push(estimate);
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// Keep only recent history
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if history.len() > self.max_history {
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history.remove(0);
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}
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}
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/// Fuse multiple position estimates into one
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pub fn fuse(&self, estimates: &[PositionEstimate]) -> Option<Coordinates3D> {
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if estimates.is_empty() {
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return None;
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}
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|
||||
if estimates.len() == 1 {
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return Some(estimates[0].position.clone());
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}
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// Weighted average based on uncertainty and source confidence
|
||||
let mut total_weight = 0.0;
|
||||
let mut sum_x = 0.0;
|
||||
let mut sum_y = 0.0;
|
||||
let mut sum_z = 0.0;
|
||||
|
||||
for estimate in estimates {
|
||||
let weight = self.calculate_weight(estimate);
|
||||
total_weight += weight;
|
||||
sum_x += estimate.position.x * weight;
|
||||
sum_y += estimate.position.y * weight;
|
||||
sum_z += estimate.position.z * weight;
|
||||
}
|
||||
|
||||
if total_weight == 0.0 {
|
||||
return None;
|
||||
}
|
||||
|
||||
let fused_x = sum_x / total_weight;
|
||||
let fused_y = sum_y / total_weight;
|
||||
let fused_z = sum_z / total_weight;
|
||||
|
||||
// Calculate fused uncertainty (reduced due to multiple estimates)
|
||||
let fused_uncertainty = self.calculate_fused_uncertainty(estimates);
|
||||
|
||||
Some(Coordinates3D::new(
|
||||
fused_x,
|
||||
fused_y,
|
||||
fused_z,
|
||||
fused_uncertainty,
|
||||
))
|
||||
}
|
||||
|
||||
/// Fuse with temporal smoothing
|
||||
pub fn fuse_with_history(&self, current: &PositionEstimate) -> Option<Coordinates3D> {
|
||||
// Add current to history
|
||||
self.add_estimate(current.clone());
|
||||
|
||||
let history = self.history.read();
|
||||
|
||||
// Use exponentially weighted moving average
|
||||
let alpha = 0.3; // Smoothing factor
|
||||
let mut smoothed = current.position.clone();
|
||||
|
||||
for (i, estimate) in history.iter().rev().enumerate().skip(1) {
|
||||
let weight = alpha * (1.0 - alpha).powi(i as i32);
|
||||
smoothed.x = smoothed.x * (1.0 - weight) + estimate.position.x * weight;
|
||||
smoothed.y = smoothed.y * (1.0 - weight) + estimate.position.y * weight;
|
||||
smoothed.z = smoothed.z * (1.0 - weight) + estimate.position.z * weight;
|
||||
}
|
||||
|
||||
Some(smoothed)
|
||||
}
|
||||
|
||||
/// Calculate weight for an estimate
|
||||
fn calculate_weight(&self, estimate: &PositionEstimate) -> f64 {
|
||||
// Base weight from source reliability
|
||||
let source_weight = match estimate.source {
|
||||
EstimateSource::TimeOfArrival => 1.0,
|
||||
EstimateSource::AngleOfArrival => 0.9,
|
||||
EstimateSource::CsiFingerprint => 0.8,
|
||||
EstimateSource::RssiTriangulation => 0.7,
|
||||
EstimateSource::DepthEstimation => 0.6,
|
||||
EstimateSource::Fused => 1.0,
|
||||
};
|
||||
|
||||
// Adjust by uncertainty (lower uncertainty = higher weight)
|
||||
let uncertainty_factor = 1.0 / (1.0 + estimate.position.uncertainty.horizontal_error);
|
||||
|
||||
// User-provided weight
|
||||
let user_weight = estimate.weight;
|
||||
|
||||
source_weight * uncertainty_factor * user_weight
|
||||
}
|
||||
|
||||
/// Calculate uncertainty after fusing multiple estimates
|
||||
fn calculate_fused_uncertainty(&self, estimates: &[PositionEstimate]) -> LocationUncertainty {
|
||||
if estimates.is_empty() {
|
||||
return LocationUncertainty::default();
|
||||
}
|
||||
|
||||
// Combined uncertainty is reduced with multiple estimates
|
||||
let n = estimates.len() as f64;
|
||||
|
||||
let avg_h_error: f64 = estimates.iter()
|
||||
.map(|e| e.position.uncertainty.horizontal_error)
|
||||
.sum::<f64>() / n;
|
||||
|
||||
let avg_v_error: f64 = estimates.iter()
|
||||
.map(|e| e.position.uncertainty.vertical_error)
|
||||
.sum::<f64>() / n;
|
||||
|
||||
// Uncertainty reduction factor (more estimates = more confidence)
|
||||
let reduction = (1.0 / n.sqrt()).max(0.5);
|
||||
|
||||
LocationUncertainty {
|
||||
horizontal_error: avg_h_error * reduction,
|
||||
vertical_error: avg_v_error * reduction,
|
||||
confidence: (0.95 * (1.0 + (n - 1.0) * 0.02)).min(0.99),
|
||||
}
|
||||
}
|
||||
|
||||
/// Clear history
|
||||
pub fn clear_history(&self) {
|
||||
self.history.write().clear();
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for PositionFuser {
|
||||
fn default() -> Self {
|
||||
Self::new()
|
||||
}
|
||||
}
|
||||
|
||||
/// Simple random range (for simulation)
|
||||
fn rand_range(min: f64, max: f64) -> f64 {
|
||||
use std::time::{SystemTime, UNIX_EPOCH};
|
||||
|
||||
let seed = SystemTime::now()
|
||||
.duration_since(UNIX_EPOCH)
|
||||
.map(|d| d.as_nanos() as u64)
|
||||
.unwrap_or(0);
|
||||
|
||||
let pseudo_random = ((seed * 1103515245 + 12345) % (1 << 31)) as f64 / (1u64 << 31) as f64;
|
||||
min + pseudo_random * (max - min)
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use chrono::Utc;
|
||||
|
||||
fn create_test_estimate(x: f64, y: f64, z: f64) -> PositionEstimate {
|
||||
PositionEstimate {
|
||||
position: Coordinates3D::with_default_uncertainty(x, y, z),
|
||||
timestamp: Utc::now(),
|
||||
source: EstimateSource::RssiTriangulation,
|
||||
weight: 1.0,
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_single_estimate_fusion() {
|
||||
let fuser = PositionFuser::new();
|
||||
let estimate = create_test_estimate(5.0, 10.0, -2.0);
|
||||
|
||||
let result = fuser.fuse(&[estimate]);
|
||||
assert!(result.is_some());
|
||||
|
||||
let pos = result.unwrap();
|
||||
assert!((pos.x - 5.0).abs() < 0.001);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_multiple_estimate_fusion() {
|
||||
let fuser = PositionFuser::new();
|
||||
|
||||
let estimates = vec![
|
||||
create_test_estimate(4.0, 9.0, -1.5),
|
||||
create_test_estimate(6.0, 11.0, -2.5),
|
||||
];
|
||||
|
||||
let result = fuser.fuse(&estimates);
|
||||
assert!(result.is_some());
|
||||
|
||||
let pos = result.unwrap();
|
||||
// Should be roughly in between
|
||||
assert!(pos.x > 4.0 && pos.x < 6.0);
|
||||
assert!(pos.y > 9.0 && pos.y < 11.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_fused_uncertainty_reduction() {
|
||||
let fuser = PositionFuser::new();
|
||||
|
||||
let estimates = vec![
|
||||
create_test_estimate(5.0, 10.0, -2.0),
|
||||
create_test_estimate(5.1, 10.1, -2.1),
|
||||
create_test_estimate(4.9, 9.9, -1.9),
|
||||
];
|
||||
|
||||
let single_uncertainty = estimates[0].position.uncertainty.horizontal_error;
|
||||
let fused_uncertainty = fuser.calculate_fused_uncertainty(&estimates);
|
||||
|
||||
// Fused should have lower uncertainty
|
||||
assert!(fused_uncertainty.horizontal_error < single_uncertainty);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_localization_service_creation() {
|
||||
let service = LocalizationService::new();
|
||||
// Just verify it creates without panic
|
||||
assert!(true);
|
||||
drop(service);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,14 @@
|
||||
//! Localization module for survivor position estimation.
|
||||
//!
|
||||
//! This module provides:
|
||||
//! - Triangulation from multiple access points
|
||||
//! - Depth estimation through debris
|
||||
//! - Position fusion combining multiple techniques
|
||||
|
||||
mod triangulation;
|
||||
mod depth;
|
||||
mod fusion;
|
||||
|
||||
pub use triangulation::{Triangulator, TriangulationConfig};
|
||||
pub use depth::{DepthEstimator, DepthEstimatorConfig};
|
||||
pub use fusion::{PositionFuser, LocalizationService};
|
||||
@@ -0,0 +1,377 @@
|
||||
//! Triangulation for 2D/3D position estimation from multiple sensors.
|
||||
|
||||
use crate::domain::{Coordinates3D, LocationUncertainty, SensorPosition};
|
||||
|
||||
/// Configuration for triangulation
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct TriangulationConfig {
|
||||
/// Minimum number of sensors required
|
||||
pub min_sensors: usize,
|
||||
/// Maximum position uncertainty to accept (meters)
|
||||
pub max_uncertainty: f64,
|
||||
/// Path loss exponent for distance estimation
|
||||
pub path_loss_exponent: f64,
|
||||
/// Reference distance for path loss model (meters)
|
||||
pub reference_distance: f64,
|
||||
/// Reference RSSI at reference distance (dBm)
|
||||
pub reference_rssi: f64,
|
||||
/// Use weighted least squares
|
||||
pub weighted: bool,
|
||||
}
|
||||
|
||||
impl Default for TriangulationConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
min_sensors: 3,
|
||||
max_uncertainty: 5.0,
|
||||
path_loss_exponent: 3.0, // Indoor with obstacles
|
||||
reference_distance: 1.0,
|
||||
reference_rssi: -30.0,
|
||||
weighted: true,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Result of a distance estimation
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct DistanceEstimate {
|
||||
/// Sensor ID
|
||||
pub sensor_id: String,
|
||||
/// Estimated distance in meters
|
||||
pub distance: f64,
|
||||
/// Estimation confidence
|
||||
pub confidence: f64,
|
||||
}
|
||||
|
||||
/// Triangulator for position estimation
|
||||
pub struct Triangulator {
|
||||
config: TriangulationConfig,
|
||||
}
|
||||
|
||||
impl Triangulator {
|
||||
/// Create a new triangulator
|
||||
pub fn new(config: TriangulationConfig) -> Self {
|
||||
Self { config }
|
||||
}
|
||||
|
||||
/// Create with default configuration
|
||||
pub fn with_defaults() -> Self {
|
||||
Self::new(TriangulationConfig::default())
|
||||
}
|
||||
|
||||
/// Estimate position from RSSI measurements
|
||||
pub fn estimate_position(
|
||||
&self,
|
||||
sensors: &[SensorPosition],
|
||||
rssi_values: &[(String, f64)], // (sensor_id, rssi)
|
||||
) -> Option<Coordinates3D> {
|
||||
// Get distance estimates from RSSI
|
||||
let distances: Vec<(SensorPosition, f64)> = rssi_values
|
||||
.iter()
|
||||
.filter_map(|(id, rssi)| {
|
||||
let sensor = sensors.iter().find(|s| &s.id == id)?;
|
||||
if !sensor.is_operational {
|
||||
return None;
|
||||
}
|
||||
let distance = self.rssi_to_distance(*rssi);
|
||||
Some((sensor.clone(), distance))
|
||||
})
|
||||
.collect();
|
||||
|
||||
if distances.len() < self.config.min_sensors {
|
||||
return None;
|
||||
}
|
||||
|
||||
// Perform trilateration
|
||||
self.trilaterate(&distances)
|
||||
}
|
||||
|
||||
/// Estimate position from Time of Arrival measurements
|
||||
pub fn estimate_from_toa(
|
||||
&self,
|
||||
sensors: &[SensorPosition],
|
||||
toa_values: &[(String, f64)], // (sensor_id, time_of_arrival_ns)
|
||||
) -> Option<Coordinates3D> {
|
||||
const SPEED_OF_LIGHT: f64 = 299_792_458.0; // m/s
|
||||
|
||||
let distances: Vec<(SensorPosition, f64)> = toa_values
|
||||
.iter()
|
||||
.filter_map(|(id, toa)| {
|
||||
let sensor = sensors.iter().find(|s| &s.id == id)?;
|
||||
if !sensor.is_operational {
|
||||
return None;
|
||||
}
|
||||
// Convert nanoseconds to distance
|
||||
let distance = (*toa * 1e-9) * SPEED_OF_LIGHT / 2.0; // Round trip
|
||||
Some((sensor.clone(), distance))
|
||||
})
|
||||
.collect();
|
||||
|
||||
if distances.len() < self.config.min_sensors {
|
||||
return None;
|
||||
}
|
||||
|
||||
self.trilaterate(&distances)
|
||||
}
|
||||
|
||||
/// Convert RSSI to distance using path loss model
|
||||
fn rssi_to_distance(&self, rssi: f64) -> f64 {
|
||||
// Log-distance path loss model:
|
||||
// RSSI = RSSI_0 - 10 * n * log10(d / d_0)
|
||||
// Solving for d:
|
||||
// d = d_0 * 10^((RSSI_0 - RSSI) / (10 * n))
|
||||
|
||||
let exponent = (self.config.reference_rssi - rssi)
|
||||
/ (10.0 * self.config.path_loss_exponent);
|
||||
|
||||
self.config.reference_distance * 10.0_f64.powf(exponent)
|
||||
}
|
||||
|
||||
/// Perform trilateration using least squares
|
||||
fn trilaterate(&self, distances: &[(SensorPosition, f64)]) -> Option<Coordinates3D> {
|
||||
if distances.len() < 3 {
|
||||
return None;
|
||||
}
|
||||
|
||||
// Use linearized least squares approach
|
||||
// Reference: https://en.wikipedia.org/wiki/Trilateration
|
||||
|
||||
// Use first sensor as reference
|
||||
let (ref_sensor, ref_dist) = &distances[0];
|
||||
let x1 = ref_sensor.x;
|
||||
let y1 = ref_sensor.y;
|
||||
let r1 = *ref_dist;
|
||||
|
||||
// Build system of linear equations: A * [x, y]^T = b
|
||||
let n = distances.len() - 1;
|
||||
let mut a_matrix = vec![vec![0.0; 2]; n];
|
||||
let mut b_vector = vec![0.0; n];
|
||||
|
||||
for (i, (sensor, dist)) in distances.iter().skip(1).enumerate() {
|
||||
let xi = sensor.x;
|
||||
let yi = sensor.y;
|
||||
let ri = *dist;
|
||||
|
||||
// Linearized equation from difference of squared distances
|
||||
a_matrix[i][0] = 2.0 * (xi - x1);
|
||||
a_matrix[i][1] = 2.0 * (yi - y1);
|
||||
b_vector[i] = r1 * r1 - ri * ri - x1 * x1 + xi * xi - y1 * y1 + yi * yi;
|
||||
}
|
||||
|
||||
// Solve using least squares: (A^T * A)^-1 * A^T * b
|
||||
let solution = self.solve_least_squares(&a_matrix, &b_vector)?;
|
||||
|
||||
// Calculate uncertainty from residuals
|
||||
let uncertainty = self.calculate_uncertainty(&solution, distances);
|
||||
|
||||
if uncertainty.horizontal_error > self.config.max_uncertainty {
|
||||
return None;
|
||||
}
|
||||
|
||||
Some(Coordinates3D::new(
|
||||
solution[0],
|
||||
solution[1],
|
||||
0.0, // Z estimated separately
|
||||
uncertainty,
|
||||
))
|
||||
}
|
||||
|
||||
/// Solve linear system using least squares
|
||||
fn solve_least_squares(&self, a: &[Vec<f64>], b: &[f64]) -> Option<Vec<f64>> {
|
||||
let n = a.len();
|
||||
if n < 2 || a[0].len() != 2 {
|
||||
return None;
|
||||
}
|
||||
|
||||
// Calculate A^T * A
|
||||
let mut ata = vec![vec![0.0; 2]; 2];
|
||||
for i in 0..2 {
|
||||
for j in 0..2 {
|
||||
for k in 0..n {
|
||||
ata[i][j] += a[k][i] * a[k][j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Calculate A^T * b
|
||||
let mut atb = vec![0.0; 2];
|
||||
for i in 0..2 {
|
||||
for k in 0..n {
|
||||
atb[i] += a[k][i] * b[k];
|
||||
}
|
||||
}
|
||||
|
||||
// Solve 2x2 system using Cramer's rule
|
||||
let det = ata[0][0] * ata[1][1] - ata[0][1] * ata[1][0];
|
||||
if det.abs() < 1e-10 {
|
||||
return None;
|
||||
}
|
||||
|
||||
let x = (atb[0] * ata[1][1] - atb[1] * ata[0][1]) / det;
|
||||
let y = (ata[0][0] * atb[1] - ata[1][0] * atb[0]) / det;
|
||||
|
||||
Some(vec![x, y])
|
||||
}
|
||||
|
||||
/// Calculate position uncertainty from residuals
|
||||
fn calculate_uncertainty(
|
||||
&self,
|
||||
position: &[f64],
|
||||
distances: &[(SensorPosition, f64)],
|
||||
) -> LocationUncertainty {
|
||||
// Calculate root mean square error
|
||||
let mut sum_sq_error = 0.0;
|
||||
|
||||
for (sensor, measured_dist) in distances {
|
||||
let dx = position[0] - sensor.x;
|
||||
let dy = position[1] - sensor.y;
|
||||
let estimated_dist = (dx * dx + dy * dy).sqrt();
|
||||
let error = measured_dist - estimated_dist;
|
||||
sum_sq_error += error * error;
|
||||
}
|
||||
|
||||
let rmse = (sum_sq_error / distances.len() as f64).sqrt();
|
||||
|
||||
// GDOP (Geometric Dilution of Precision) approximation
|
||||
let gdop = self.estimate_gdop(position, distances);
|
||||
|
||||
LocationUncertainty {
|
||||
horizontal_error: rmse * gdop,
|
||||
vertical_error: rmse * gdop * 1.5, // Vertical typically less accurate
|
||||
confidence: 0.95,
|
||||
}
|
||||
}
|
||||
|
||||
/// Estimate Geometric Dilution of Precision
|
||||
fn estimate_gdop(&self, position: &[f64], distances: &[(SensorPosition, f64)]) -> f64 {
|
||||
// Simplified GDOP based on sensor geometry
|
||||
let mut sum_angle = 0.0;
|
||||
let n = distances.len();
|
||||
|
||||
for i in 0..n {
|
||||
for j in (i + 1)..n {
|
||||
let dx1 = distances[i].0.x - position[0];
|
||||
let dy1 = distances[i].0.y - position[1];
|
||||
let dx2 = distances[j].0.x - position[0];
|
||||
let dy2 = distances[j].0.y - position[1];
|
||||
|
||||
let dot = dx1 * dx2 + dy1 * dy2;
|
||||
let mag1 = (dx1 * dx1 + dy1 * dy1).sqrt();
|
||||
let mag2 = (dx2 * dx2 + dy2 * dy2).sqrt();
|
||||
|
||||
if mag1 > 0.0 && mag2 > 0.0 {
|
||||
let cos_angle = (dot / (mag1 * mag2)).clamp(-1.0, 1.0);
|
||||
let angle = cos_angle.acos();
|
||||
sum_angle += angle;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Average angle between sensor pairs
|
||||
let num_pairs = (n * (n - 1)) as f64 / 2.0;
|
||||
let avg_angle = if num_pairs > 0.0 {
|
||||
sum_angle / num_pairs
|
||||
} else {
|
||||
std::f64::consts::PI / 4.0
|
||||
};
|
||||
|
||||
// GDOP is better when sensors are spread out (angle closer to 90 degrees)
|
||||
// GDOP gets worse as sensors are collinear
|
||||
let optimal_angle = std::f64::consts::PI / 2.0;
|
||||
let angle_factor = (avg_angle / optimal_angle - 1.0).abs() + 1.0;
|
||||
|
||||
angle_factor.max(1.0)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::domain::SensorType;
|
||||
|
||||
fn create_test_sensors() -> Vec<SensorPosition> {
|
||||
vec![
|
||||
SensorPosition {
|
||||
id: "s1".to_string(),
|
||||
x: 0.0,
|
||||
y: 0.0,
|
||||
z: 1.5,
|
||||
sensor_type: SensorType::Transceiver,
|
||||
is_operational: true,
|
||||
},
|
||||
SensorPosition {
|
||||
id: "s2".to_string(),
|
||||
x: 10.0,
|
||||
y: 0.0,
|
||||
z: 1.5,
|
||||
sensor_type: SensorType::Transceiver,
|
||||
is_operational: true,
|
||||
},
|
||||
SensorPosition {
|
||||
id: "s3".to_string(),
|
||||
x: 5.0,
|
||||
y: 10.0,
|
||||
z: 1.5,
|
||||
sensor_type: SensorType::Transceiver,
|
||||
is_operational: true,
|
||||
},
|
||||
]
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_rssi_to_distance() {
|
||||
let triangulator = Triangulator::with_defaults();
|
||||
|
||||
// At reference distance, RSSI should equal reference RSSI
|
||||
let distance = triangulator.rssi_to_distance(-30.0);
|
||||
assert!((distance - 1.0).abs() < 0.1);
|
||||
|
||||
// Weaker signal = further distance
|
||||
let distance2 = triangulator.rssi_to_distance(-60.0);
|
||||
assert!(distance2 > distance);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_trilateration() {
|
||||
let triangulator = Triangulator::with_defaults();
|
||||
let sensors = create_test_sensors();
|
||||
|
||||
// Target at (5, 4) - calculate distances
|
||||
let target = (5.0, 4.0);
|
||||
let distances: Vec<(&str, f64)> = vec![
|
||||
("s1", ((target.0 - 0.0).powi(2) + (target.1 - 0.0).powi(2)).sqrt()),
|
||||
("s2", ((target.0 - 10.0).powi(2) + (target.1 - 0.0).powi(2)).sqrt()),
|
||||
("s3", ((target.0 - 5.0).powi(2) + (target.1 - 10.0).powi(2)).sqrt()),
|
||||
];
|
||||
|
||||
let dist_vec: Vec<(SensorPosition, f64)> = distances
|
||||
.iter()
|
||||
.filter_map(|(id, d)| {
|
||||
let sensor = sensors.iter().find(|s| s.id == *id)?;
|
||||
Some((sensor.clone(), *d))
|
||||
})
|
||||
.collect();
|
||||
|
||||
let result = triangulator.trilaterate(&dist_vec);
|
||||
assert!(result.is_some());
|
||||
|
||||
let pos = result.unwrap();
|
||||
assert!((pos.x - target.0).abs() < 0.5);
|
||||
assert!((pos.y - target.1).abs() < 0.5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_insufficient_sensors() {
|
||||
let triangulator = Triangulator::with_defaults();
|
||||
let sensors = create_test_sensors();
|
||||
|
||||
// Only 2 distance measurements
|
||||
let rssi_values = vec![
|
||||
("s1".to_string(), -40.0),
|
||||
("s2".to_string(), -45.0),
|
||||
];
|
||||
|
||||
let result = triangulator.estimate_position(&sensors, &rssi_values);
|
||||
assert!(result.is_none());
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user