自适应扩展卡尔曼滤波代码

根据提供的引用内容，我无法确定您需要的自适应扩展卡尔曼滤波的具体实现。但是，我可以为您提供一个通用的卡尔曼滤波算法的Python实现，供您参考：

import numpy as np

class KalmanFilter:
    def __init__(self, F, H, Q, R, x0, P0):
        self.F = F
        self.H = H
        self.Q = Q
        self.R = R
        self.x = x0
        self.P = P0

    def predict(self):
        self.x = np.dot(self.F, self.x)
        self.P = np.dot(np.dot(self.F, self.P), self.F.T) + self.Q

    def update(self, z):
        y = z - np.dot(self.H, self.x)
        S = np.dot(np.dot(self.H, self.P), self.H.T) + self.R
        K = np.dot(np.dot(self.P, self.H.T), np.linalg.inv(S))
        self.x = self.x + np.dot(K, y)
        I = np.eye(self.F.shape[0])
        self.P = np.dot(np.dot(I - np.dot(K, self.H), self.P), (I - np.dot(K, self.H)).T) + np.dot(np.dot(K, self.R), K.T)

# 示例
dt = 1.0/60.0

# 状态转移矩阵
F = np.array([[1, dt, 0], [0, 1, dt], [0, 0, 1]])

# 观测矩阵
H = np.array([1, 0, 0]).reshape(1, 3)

# 过程噪声协方差
q = 1e-5
Q = np.array([[q, 0, 0], [0, q, 0], [0, 0, q]])

# 测量噪声协方差
r = 0.01
R = np.array([r]).reshape(1, 1)

# 初始状态
x = np.array([0, 0, 0]).reshape(3, 1)

# 初始协方差矩阵
P = np.diag([100, 100, 100])

kf = KalmanFilter(F=F, H=H, Q=Q, R=R, x0=x, P0=P)

# 生成模拟数据
times = np.arange(0, 10, dt)
n_samples = len(times)
xs = np.zeros((n_samples, 3))
ys = np.zeros((n_samples, 1))
for i in range(n_samples):
    t = times[i]
    xs[i] = np.array([0.01*t**2, 0.1*t, 10*np.sin(t)])
    ys[i] = np.dot(H, xs[i]) + r*np.random.randn()

# 进行卡尔曼滤波
filtered_xs = np.zeros((n_samples, 3))
for i in range(n_samples):
    kf.predict()
    kf.update(ys[i])
    filtered_xs[i] = kf.x.T

# 输出结果
print(filtered_xs)