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