用c语言写一个卡尔曼滤波函数
时间: 2024-04-30 15:17:42 浏览: 50
好的,您可以使用以下代码编写卡尔曼滤波函数:
```c
#include <stdio.h>
#include <math.h>
#define N 2 //状态量(2),可根据需要改变
#define M 1 //观测量(1), 可根据需要改变
void KalmanFilter(float* z, float* x)
{
float A[N][N] = {{1, 1}, {0, 1}}; //状态转移矩阵
float H[M][N] = {{1, 0}}; //观测矩阵
float Q[N][N] = {{0.0001, 0}, {0, 0.0001}}; //过程噪声协方差
float R[M][M] = {{0.005}}; //观测噪声协方差
float P[N][N] = {{1, 0}, {0, 1}}; //后验误差协方差
float I[N][N] = {{1, 0}, {0, 1}}; //单位矩阵
float u[N] = {0}; //外部状态输入为0
float K[N][M]; //卡尔曼增益
float y[M]; //残差
float S[M][M]; //残差协方差
float P_temp[N][N]; //临时矩阵
int i, j, k;
for (k = 0; k < 100; k++) //100个时刻
{
//预测
for (i = 0; i < N; i++) {
u[i] = 0; //外部输入设为0
x[i] = 0; //清零x
for (j = 0; j < N; j++)
x[i] += A[i][j] * x[j];
x[i] += u[i];
}
for (i = 0; i < N; i++)
for (j = 0; j < N; j++)
P_temp[i][j] = 0;
for (i = 0; i < N; i++)
for (j = 0; j < N; j++)
P_temp[i][j] += A[i][k] * P[k][j];
for (i = 0; i < N; i++)
for (j = 0; j < N; j++)
P[i][j] = 0;
for (i = 0; i < N; i++)
for (j = 0; j < N; j++)
P[i][j] += P_temp[i][j] * A[k][j];
for (i = 0; i < N; i++)
for (j = 0; j < M; j++)
y[j] = z[j] - H[j][k] * x[k]; //计算残差
//计算卡尔曼增益
for (i = 0; i < N; i++)
for (j = 0; j < M; j++)
S[i][j] = 0;
for (i = 0; i < M; i++)
for (j = 0; j < N; j++)
S[i][j] += H[i][k] * P[k][j];
for (i = 0; i < M; i++)
for (j = 0; j < M; j++)
S[i][j] += R[i][j];
for (i = 0; i < N; i++)
for (j = 0; j < M; j++)
K[i][j] = 0;
for (i = 0; i < N; i++)
for (j = 0; j < M; j++)
for (k = 0; k < M; k++)
K[i][j] += P[k][i] * H[k][j] / S[k][k];
//更新
for (i = 0; i < N; i++)
x[i] += K[i][k] * y[k];
for (i = 0; i < N; i++)
for (j = 0; j < N; j++)
P_temp[i][j] = 0;
for (i = 0; i < N; i++)
for (j = 0; j < N; j++)
P_temp[i][j] += I[i][j] - K[i][k] * H[k][j];
for (i = 0; i < N; i++)
for (j = 0; j < N; j++)
P[i][j] = 0;
for (i = 0; i < N; i++)
for (j = 0; j < N; j++)
for (k = 0; k < N; k++)
P[i][j] += P_temp[i][k] * P[k][j];
//输出
printf("%f\n", x[0]);
}
}
int main(void)
{
float z[1] = {0.8496}; //观测值
float x[N] = {0.1, 0.1}; //状态值
KalmanFilter(z, x);
return 0;
}
```
这个函数可以通过KalmanFilter()函数调用,其中z是观测值,x是状态值。函数中的参数和变量名称可以自由修改。
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