自适应卡尔曼滤波代码

根据提供的引用内容，以下是一个自适应卡尔曼滤波的代码示例：

# 初始化参数
n_x = size(X,1)
R_x = 2.5e-5
P_x = diag(1e-10)
Q_x = \[1.0000e-10 0 0; 0 1.0000e-10 0; 0 0 1.0000e-10\]

# 初始化输出向量
SOC_Estimated = \[\]
Vt_Estimated = \[\]
Vt_Error = \[\]

# 根据当前向量的大小设置for循环的长度
ik = length(Current)

# 卡尔曼滤波算法
for i in range(ik):
    # 预测步骤
    X_predicted = F*X
    P_predicted = F*P*F' + Q
    
    # 校正步骤
    K = P_predicted*H'/(H*P_predicted*H' + R)
    X_corrected = X_predicted + K*(Z - H*X_predicted)
    P_corrected = (eye(n_x) - K*H)*P_predicted
    
    # 更新参数
    X = X_corrected
    P = P_corrected
    
    # 存储估计值
    SOC_Estimated.append(X\[1\])
    Vt_Estimated.append(X\[2\])
    Vt_Error.append(Z - H*X_predicted)

请注意，这只是一个示例代码，具体的实现可能会根据具体的应用场景和需求而有所不同。在实际使用时，您可能需要根据您的数据和系统模型进行适当的调整和优化。
#### 引用[.reference_title]
- *1* *2* [基于自适应扩展卡尔曼滤波器（AEKF）的锂离子电池SOC估计（附MATLAB代码）](https://blog.csdn.net/m0_60354177/article/details/127890424)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^koosearch_v1,239^v3^insert_chatgpt"}} ] [.reference_item]
- *3* [【笔记】自适应卡尔曼滤波 Adaptive Extended Kalman Filter](https://blog.csdn.net/zhoupian/article/details/125749340)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^koosearch_v1,239^v3^insert_chatgpt"}} ] [.reference_item]
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