C++车速卡尔曼滤波

卡尔曼滤波是一种用于估计系统状态的滤波算法，可以在测量数据和系统模型之间进行融合，提供更准确的状态估计。在车速估计中，卡尔曼滤波可以用于融合车辆传感器（如车速传感器、陀螺仪等）的测量数据，从而减小测量误差，并提供更稳定和准确的车速估计结果。

下面是一个简单的使用C++实现的车速卡尔曼滤波的示例代码：

#include <iostream>
#include <cmath>

// 卡尔曼滤波器类
class KalmanFilter {
public:
    KalmanFilter(double initial_state, double initial_variance, double process_variance, double measurement_variance)
        : state(initial_state), variance(initial_variance),
          processVariance(process_variance), measurementVariance(measurement_variance) {}

    void update(double measurement) {
        // 预测步骤
        double predicted_state = state;
        double predicted_variance = variance + processVariance;

        // 更新步骤
        double kalman_gain = predicted_variance / (predicted_variance + measurementVariance);
        state = predicted_state + kalman_gain * (measurement - predicted_state);
        variance = (1 - kalman_gain) * predicted_variance;

        std::cout << "Filtered state: " << state << std::endl;    }

private:
    double state;               // 状态估计
    double variance;            // 方差
    double processVariance;     // 过程噪声方差
    double measurementVariance; // 测量噪声方差
};

int main() {
    double initial_state = 0.0;          // 初始状态
    double initial_variance = 1.0;       // 初始方差
    double process_variance = 0.1;       // 过程噪声方差
    double measurement_variance = 0.5;   // 测量噪声方差

    KalmanFilter kf(initial_state, initial_variance, process_variance, measurement_variance);

    double measurements[] = {1.2, 1.6, 2.1, 2.4};   // 测量数据

    for (double measurement : measurements) {
        kf.update(measurement);
    }

    return 0;
}