sage-husa卡尔曼算法c代码实现
时间: 2024-05-14 13:17:58 浏览: 148
卡尔曼算法C语言实现
以下是基于SAGE-HUSA算法的卡尔曼滤波器的C代码实现:
```c
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#define DIM 2
// 状态空间模型
double A[DIM][DIM] = {{1, 1}, {0, 1}};
double H[DIM] = {1, 0};
double Q[DIM][DIM] = {{0.01, 0}, {0, 0.01}};
double R = 0.1;
double x_hat[DIM] = {0};
double P[DIM][DIM] = {{1000, 0}, {0, 1000}};
// SAGE-HUSA算法参数
double alpha = 0.99;
double beta = 0.5;
double gamma = 0.5;
double delta = 0.5;
// 生成高斯随机数
double gaussrand()
{
static double V1, V2, S;
static int phase = 0;
double X;
if (phase == 0) {
do {
double U1 = (double) rand() / RAND_MAX;
double U2 = (double) rand() / RAND_MAX;
V1 = 2 * U1 - 1;
V2 = 2 * U2 - 1;
S = V1 * V1 + V2 * V2;
} while (S >= 1 || S == 0);
X = V1 * sqrt(-2 * log(S) / S);
} else {
X = V2 * sqrt(-2 * log(S) / S);
}
phase = 1 - phase;
return X;
}
int main()
{
int i, j;
double y, K[DIM], S;
double x_predict[DIM], P_predict[DIM][DIM], P_update[DIM][DIM];
for (i = 0; i < 100; i++) {
// 生成观测值
y = H[0] * x_hat[0] + H[1] * x_hat[1] + gaussrand() * sqrt(R);
// 预测值
for (j = 0; j < DIM; j++) {
x_predict[j] = A[j][0] * x_hat[0] + A[j][1] * x_hat[1];
}
for (j = 0; j < DIM; j++) {
x_hat[j] = x_predict[j];
}
// 更新P值
for (j = 0; j < DIM; j++) {
for (int k = 0; k < DIM; k++) {
P_predict[j][k] = A[j][0] * P[0][k] + A[j][1] * P[1][k];
}
}
for (j = 0; j < DIM; j++) {
for (int k = 0; k < DIM; k++) {
P[j][k] = P_predict[j][k] + Q[j][k];
}
}
// 计算S值
S = H[0] * P[0][0] * H[0] + R;
// 计算Kalman增益
K[0] = P[0][0] * H[0] / S;
K[1] = P[1][0] * H[0] / S;
// 更新状态值
x_hat[0] = x_hat[0] + K[0] * (y - H[0] * x_hat[0]);
x_hat[1] = x_hat[1] + K[1] * (y - H[0] * x_hat[0]);
// 更新P值
P_update[0][0] = (1 - K[0] * H[0]) * P[0][0] - K[0] * H[1] * P[1][0];
P_update[0][1] = (1 - K[0] * H[0]) * P[0][1] - K[0] * H[1] * P[1][1];
P_update[1][0] = (1 - K[1] * H[0]) * P[1][0] - K[1] * H[1] * P[1][0];
P_update[1][1] = (1 - K[1] * H[0]) * P[1][1] - K[1] * H[1] * P[1][1];
P[0][0] = alpha * P_update[0][0] + beta * Q[0][0] + gamma * K[0] * H[0] * P[0][0] + delta * K[0] * H[1] * P[1][0];
P[0][1] = alpha * P_update[0][1] + beta * Q[0][1] + gamma * K[0] * H[0] * P[0][1] + delta * K[0] * H[1] * P[1][1];
P[1][0] = alpha * P_update[1][0] + beta * Q[1][0] + gamma * K[1] * H[0] * P[0][0] + delta * K[1] * H[1] * P[1][0];
P[1][1] = alpha * P_update[1][1] + beta * Q[1][1] + gamma * K[1] * H[0] * P[0][1] + delta * K[1] * H[1] * P[1][1];
printf("%lf\n", x_hat[0]);
}
return 0;
}
```
其中,高斯随机数生成函数 `gaussrand()` 参考了以下代码:
```c
double gaussrand()
{
static double V1, V2, S;
static int phase = 0;
double X;
if (phase == 0) {
do {
double U1 = (double) rand() / RAND_MAX;
double U2 = (double) rand() / RAND_MAX;
V1 = 2 * U1 - 1;
V2 = 2 * U2 - 1;
S = V1 * V1 + V2 * V2;
} while (S >= 1 || S == 0);
X = V1 * sqrt(-2 * log(S) / S);
} else {
X = V2 * sqrt(-2 * log(S) / S);
}
phase = 1 - phase;
return X;
}
```
使用时,可以根据实际情况修改状态空间模型和算法参数。
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