int16 Kalman_filter(int16 ADC_Value)//卡尔曼滤波 { float LastData; float NowData; float kalman_adc; static float kalman_adc_old=0; static float P1; //后验估计协方差 static float Q=2; //系统协方差 static float R=100; //测量协方差 static float Kg=0; //卡尔曼增益 static float P=1; //先验估计协方差 //预测过程 NowData=(float)ADC_Value; LastData=kalman_adc_old; P=P1+Q; //更新过程 Kg=P/(P+R); kalman_adc = LastData+Kg*(NowData-kalman_adc_old); P1=(1-Kg)*P; P=P1; kalman_adc_old=kalman_adc; return (int16)kalman_adc; }

这是一个基于卡尔曼滤波算法的函数，用于对传感器采集到的ADC值进行滤波处理。函数输入参数为ADC值，输出为滤波后的ADC值。

在函数中，定义了一些静态变量，包括后验估计协方差P1、系统协方差Q、测量协方差R、卡尔曼增益Kg、先验估计协方差P。其中，系统协方差Q和测量协方差R需要根据具体应用场景进行调整。

函数中实现了卡尔曼滤波算法的两个步骤：预测过程和更新过程。预测过程用于根据上一时刻的状态预测当前时刻的状态，先验估计协方差P用于描述系统状态的不确定性。更新过程用于根据当前时刻的测量值和预测值，计算出滤波后的状态值和后验估计协方差P1。卡尔曼增益Kg用于权衡预测值和测量值的权重。

最后，将滤波后的ADC值转换为int16类型并返回。

相关问题

import pandas as pd # 导入pandas工具库 import matplotlib.pyplot as plt pd.set_option('display.max_columns', 1000) pd.set_option('display.max_rows', 1000) pd.set_option('display.max_colwidth', 1000) import numpy as np import matplotlib.pyplot as plt """ Q 系统噪声 R 测量噪声 X(k|k-1) 上一次状态预测结果 X(k-1|k-1) 上一时刻的最优预测值 P(k|k-1) X(k|k-1)对应的convariance协方差 P(k-1|k-1) X(k-1|k-1) 对应的convariance协方差 """ x_last = 0 p_last = 0 Q = 0.01 # 系统噪声 R = 0.5 # 测量噪声 def kalman(z_measure, x_last=0, p_last=0, Q=0.018, R=0.0542): x_mid = x_last p_mid = p_last + Q kg = p_mid / (p_mid + R) x_now = x_mid + kg * (z_measure - x_mid) p_now = (1 - kg) * p_mid p_last = p_now x_last = x_now return x_now, p_last, x_last path = 'C:/Users/asus/Desktop/第4章作业/卡尔曼滤波数据.xlsx' data_B = pd.read_excel(path, header=None) for j in range(8): xj = list(data_B.iloc[::, j]) yj=[] for i in range(len(xj)): pred, p_last, x_last = kalman(xj[i], x_last, p_last, Q, R) yj.append(pred) ax1 = plt.subplot(1,2,1) ax2 = plt.subplot(1,2,2) plt.sca(ax1) plt.plot(xj, color="g") # 测量值 plt.sca(ax2) plt.plot(yj, color="r") # 预测值 plt.show() 把上面的pyton代码转为matlab代码

以下是将上述Python代码转换为MATLAB代码的版本：

% 导入pandas工具库
% 设置显示选项
format long
x_last = 0;
p_last = 0;
Q = 0.01; % 系统噪声
R = 0.5; % 测量噪声

function [x_now, p_last, x_last] = kalman(z_measure, x_last, p_last, Q, R)
    x_mid = x_last;
    p_mid = p_last + Q;
    kg = p_mid / (p_mid + R);
    x_now = x_mid + kg * (z_measure - x_mid);
    p_now = (1 - kg) * p_mid;
    p_last = p_now;
    x_last = x_now;
end

path = 'C:\Users\asus\Desktop\第4章作业\卡尔曼滤波数据.xlsx';
data_B = xlsread(path);

figure;
for j = 1:8
    xj = data_B(:, j);
    yj = zeros(size(xj));
    for i = 1:length(xj)
        [pred, p_last, x_last] = kalman(xj(i), x_last, p_last, Q, R);
        yj(i) = pred;
    end
    
    subplot(1,2,1);
    plot(xj, 'g'); hold on; % 测量值
    subplot(1,2,2);
    plot(yj, 'r'); hold on; % 预测值
end

hold off;

请注意，这段MATLAB代码的绘图部分使用了`subplot`和`hold`函数来创建多个

卡尔曼滤波c语言代码

以下是一个使用C语言编写的卡尔曼滤波算法的示例代码：

#include "stdio.h"
#include "stdlib.h"
#include "math.h"

#define kal_Q 0.001 /*过程噪声协方差,Q增大，动态响应变快，收敛稳定性变坏*/
#define kal_R 0.36 /*观测噪声协方差,R增大，动态响应变慢，收敛稳定性变好*/

double Kalman(double measure, int op_flg) {
    double x_mid, kg, p_mid;
    static double x_last = 0, p_last = 0;
    double x_now, p_now;

    if(op_flg) /*非初始化*/
    {
        x_mid = x_last;
        p_mid = p_last + kal_Q;
        kg = p_mid / (p_mid + kal_R);
        x_now = x_mid + kg * (measure - x_mid);
        p_now = (1 - kg) * p_mid;
        p_last = p_now;
        x_last = x_now;
    }
    else {
        x_last = measure;
        p_last = kal_Q;
    }

    return x_now;
}

int main() {
    float z_real = {5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5}; //此处替换成实际传感器采集数据,采集数据填充可以做成滑动的，先入先出
    float z_measure, x_now;
    int i;

    Kalman(z_real<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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