Sensors Fusion for Drones in C++ Using a Kalman Filter

#include <iostream>
#include <cstdlib>
#include <vector>

#define NUM_SENSORS 3 // Number of sensors being used

struct SensorData {
  double value; // Sensor measurement value
  double uncertainty; // Uncertainty of the measurement
};

// Kalman filter function
// Takes a vector of SensorData structures as input and returns a fused measurement value
double KalmanFilter(std::vector<SensorData> sensorData) {
  double fusedValue = 0.0; // Fused measurement value
  double fusedUncertainty = 0.0; // Fused measurement uncertainty

  // Iterate over each sensor
  for (int i = 0; i < NUM_SENSORS; i++) {
    double sensorValue = sensorData[i].value;
    double sensorUncertainty = sensorData[i].uncertainty;

    // Calculate the Kalman gain
    double gain = sensorUncertainty / (sensorUncertainty + fusedUncertainty);

    // Update the fused value and uncertainty
    fusedValue = fusedValue + gain * (sensorValue - fusedValue);
    fusedUncertainty = (1 - gain) * fusedUncertainty;
  }

  return fusedValue;
}

int main() {
  std::vector<SensorData> sensorData(NUM_SENSORS);

  // Get data from each sensor
  // (assume this is done using some other function or hardware)
  sensorData[0].value = 10.0;
  sensorData[0].uncertainty = 1.0;
  sensorData[1].value = 11.0;
  sensorData[1].uncertainty = 1.5;
  sensorData[2].value = 9.0;
  sensorData[2].uncertainty = 0.5;

  // Fuse the sensor data using the Kalman filter
  double fusedValue = KalmanFilter(sensorData);

  std::cout << "Fused value: " << fusedValue << std::endl;

  return 0;
}