Advanced Sensor Applications

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Sensor Fusion

Sensor fusion combines data from multiple sensors to achieve more accurate and reliable measurements than would be possible using a single sensor.

Fusion Techniques
  • Kalman filtering
  • Complementary filtering
  • Bayesian fusion
  • Weighted averaging
  • Machine learning fusion
Implementation
// Kalman filter implementation
class KalmanFilter {
  private:
    float Q = 0.1;  // Process noise
    float R = 0.1;  // Measurement noise
    float P = 1.0;  // Estimation error
    float K = 0.0;  // Kalman gain
    float X = 0.0;  // State estimate
    
  public:
    float update(float measurement) {
      // Prediction
      P = P + Q;
      
      // Update
      K = P / (P + R);
      X = X + K * (measurement - X);
      P = (1 - K) * P;
      
      return X;
    }
};