仅能测得角度，用C语言写一段X轴Y轴目标跟踪kalman滤波算法

好的，这是一个简单的X轴Y轴目标跟踪Kalman滤波算法，使用C语言编写：

#include <stdio.h>
#include <math.h>

#define PI 3.14159265f

// Kalman filter variables
float Q_angle = 0.001f;
float Q_gyro = 0.003f;
float R_angle = 0.03f;
float x_angle = 0.0f;
float x_bias = 0.0f;
float y_angle = 0.0f;
float y_bias = 0.0f;
float P_00 = 0.0f;
float P_01 = 0.0f;
float P_10 = 0.0f;
float P_11 = 0.0f;
float y_x, y_y, S_x, S_y;
float K_x_0, K_x_1, K_y_0, K_y_1;

// Sensor variables
float acc_x = 0.0f;
float acc_y = 0.0f;
float dt = 0.01f;
float x_target = 0.0f;
float y_target = 0.0f;

// Function declarations
void kalmanFilter(float acc_x, float acc_y);

int main() {
    // Sample data
    float x_meas = 0.5f;
    float y_meas = 0.5f;

    // Run Kalman filter
    kalmanFilter(x_meas, y_meas);

    // Update target positions
    x_target += x_angle;
    y_target += y_angle;

    // Print results
    printf("X target: %f\n", x_target);
    printf("Y target: %f\n", y_target);

    return 0;
}

void kalmanFilter(float acc_x, float acc_y) {
    // Predict X state
    x_angle += dt * (acc_x - x_bias);
    P_00 += -dt * (P_10 + P_01) + Q_angle * dt;
    P_01 += -dt * P_11;
    P_10 += -dt * P_11;
    P_11 += +Q_gyro * dt;

    // Update X state
    y_x = x_target - x_angle;
    S_x = P_00 + R_angle;
    K_x_0 = P_00 / S_x;
    K_x_1 = P_10 / S_x;

    x_angle += K_x_0 * y_x;
    x_bias += K_x_1 * y_x;
    P_00 -= K_x_0 * P_00;
    P_01 -= K_x_0 * P_01;
    P_10 -= K_x_1 * P_00;
    P_11 -= K_x_1 * P_01;

    // Predict Y state
    y_angle += dt * (acc_y - y_bias);
    P_00 += -dt * (P_10 + P_01) + Q_angle * dt;
    P_01 += -dt * P_11;
    P_10 += -dt * P_11;
    P_11 += +Q_gyro * dt;

    // Update Y state
    y_y = y_target - y_angle;
    S_y = P_00 + R_angle;
    K_y_0 = P_00 / S_y;
    K_y_1 = P_10 / S_y;

    y_angle += K_y_0 * y_y;
    y_bias += K_y_1 * y_y;
    P_00 -= K_y_0 * P_00;
    P_01 -= K_y_0 * P_01;
    P_10 -= K_y_1 * P_00;
    P_11 -= K_y_1 * P_01;
}

这段代码实现了一个简单的X轴Y轴目标跟踪Kalman滤波器，使用加速度计数据来估计目标位置。它包括预测状态和更新状态两个步骤，以及一些卡尔曼滤波器变量和传感器变量。在主函数中，通过不断更新目标位置，可以实现简单的目标跟踪功能。