Drone

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

Here is an example of how you might implement sensor 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;
}

This code defines a SensorData struct to represent a sensor measurement, and a function called KalmanFilter() that takes a vector of SensorData structures as input and returns a fused measurement value using a Kalman filter.

In the main function, data is retrieved from each sensor (assumed to be done using some other function or hardware). Then, the KalmanFilter() function is called to fuse the data from all three sensors. The fused value is then output to the console.

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