高斯滤波、均值滤波、savgol滤波python程序

最新推荐文章于 2025-06-23 10:44:42 发布

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本文深入探讨了卡尔曼滤波算法的实现，通过具体代码示例展示了如何处理传感器数据，实现噪声抑制和平滑。同时，介绍了中值滤波和平滑滤波的应用，帮助读者理解不同数据处理方法的优缺点。

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def kalman_filter(self, z):
        '''
        :param z: 待滤波的数组
        :return: 滤波之后的数组
        '''
        # intial parameters
        n_iter = len(z)
        sz = (n_iter,)  # size of array
        x = -0.37727  # truth value (typo in example at top of p. 13 calls this z)
        Q = 1e-5  # process variance

        # allocate space for arrays
        xhat = np.zeros(sz)  # a posteri estimate of x
        P = np.zeros(sz)  # a posteri error estimate
        xhatminus = np.zeros(sz)  # a priori estimate of x
        Pminus = np.zeros(sz)  # a priori error estimate
        K = np.zeros(sz)  # gain or blending factor

        R = 0.1 ** 2  # estimate of measurement variance, change to see effect

        # intial guesses
        xhat[0] = 0.0
        P[0] = 1.0

        for k in range(1, len(z)):
            # time update
            xhatminus[k] = xhat[k - 1]  # X(k|k-1) = AX(k-1|k-1) + BU(k) + W(k),A=1,BU(k) = 0
            Pminus[k] = P[k - 1] + Q  # P(k|k-1) = AP(k-1|k-1)A' + Q(k) ,A=1

            # measurement update
            K[k] = Pminus[k] / (Pminus[k] + R)  # Kg(k)=P(k|k-1)H'/[HP(k|k-1)H' + R],H=1
            xhat[k] = xhatminus[k] + K[k] * (z[k] - xhatminus[k])  # X(k|k) = X(k|k-1) + Kg(k)[Z(k) - HX(k|k-1)], H=1
            P[k] = (1 - K[k]) * Pminus[k]  # P(k|k) = (1 - Kg(k)H)P(k|k-1), H=1
        return xhat

#####################################################
def my_media_filter(data):
    index_data_x = len(data)
    # print(index_data_x)
    dat = np.mean(data)
    # print(dat)
    for m in range(index_data_x):
        if data[m] > dat + 0.4:
            data[m] = dat
        elif data[m] < dat - 0.4:
            data[m] = dat
        else:
            pass
    return data

###################################################
from scipy.signal import savgol_filter
savgol_filter(data, 9, 3)