ekf c语言代码
时间: 2023-07-29 09:09:16 浏览: 158
EKF,即扩展卡尔曼滤波器(Extended Kalman Filter),是一种对非线性系统进行状态估计的算法。以下是一个简单的 EKF C 语言代码示例,仅供参考:
```
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
/* EKF参数定义 */
#define dt 0.1
#define L 1.0
#define sigma_a 1.0
#define sigma_yaw 0.1
#define sigma_v 0.1
/* 状态向量定义 */
typedef struct {
double x;
double y;
double yaw;
double v;
} state_t;
/* 测量向量定义 */
typedef struct {
double x;
double y;
} measurement_t;
/* EKF算法主函数 */
void ekf(state_t *state, measurement_t *measurement) {
/* 预测 */
state->x += state->v * cos(state->yaw) * dt;
state->y += state->v * sin(state->yaw) * dt;
state->yaw += state->v / L * tan(measurement->yaw) * dt;
state->v += measurement->v * dt;
/* 更新 */
double H[2][4] = {{1, 0, 0, 0}, {0, 1, 0, 0}};
double R[2][2] = {{sigma_v*sigma_v, 0}, {0, sigma_yaw*sigma_yaw}};
double K[4][2];
double P[4][4] = {{sigma_a*sigma_a, 0, 0, 0}, {0, sigma_a*sigma_a, 0, 0}, {0, 0, sigma_yaw*sigma_yaw, 0}, {0, 0, 0, sigma_v*sigma_v}};
double S[2][2];
double y[2] = {measurement->x - state->x, measurement->y - state->y};
/* 计算卡尔曼增益 */
for (int i = 0; i < 4; i++) {
for (int j = 0; j < 2; j++) {
K[i][j] = P[i][j] * H[j][i] / (H[0][i]*P[0][i] + H[1][i]*P[1][i] + H[0][i]*P[2][i] + H[1][i]*P[3][i] + R[j][j]);
}
}
/* 更新状态估计值 */
for (int i = 0; i < 4; i++) {
state[i] += K[0][i] * y[0] + K[1][i] * y[1];
}
/* 更新协方差矩阵 */
for (int i = 0; i < 4; i++) {
for (int j = 0; j < 4; j++) {
P[i][j] = P[i][j] - K[i][0]*H[0][j]*P[0][j] - K[i][1]*H[1][j]*P[1][j];
}
}
}
int main() {
/* 初始化状态向量和测量向量 */
state_t state = {0, 0, 0, 1.0};
measurement_t measurement = {0, 0};
/* EKF算法循环 */
for (int i = 0; i < 100; i++) {
/* 生成测量向量 */
measurement.x = state.x + sigma_v * rand()/RAND_MAX;
measurement.y = state.y + sigma_v * rand()/RAND_MAX;
/* 生成控制向量 */
double a = sigma_a * rand()/RAND_MAX;
double yaw_rate = sigma_yaw * rand()/RAND_MAX;
/* 计算状态向量 */
state.v += a * dt;
state.yaw += yaw_rate * dt;
ekf(&state, &measurement);
/* 打印状态向量 */
printf("%lf %lf %lf %lf\n", state.x, state.y, state.yaw, state.v);
}
return 0;
}
```
上述代码实现了 EKF 的一个简单模拟。在实际应用中,您需要根据具体的问题场景,对 EKF 算法进行相应的参数调整和改进。
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