![](https://csdnimg.cn/release/download_crawler_static/86238883/bg5.jpg)
还有下面一个 Matlab 源程序,显示效果更好。
clear
clc;
N=300;
CON = 25;%房间温度,假定温度是恒定的
%%%%%%%%%%%%%%%kalman filter%%%%%%%%%%%%%%%%%%%%%%
x = zeros(1,N);
y = 2^0.5 * randn(1,N) + CON;%加过程噪声的状态输出
x(1) = 1;
p = 10;
Q = cov(randn(1,N));%过程噪声协方差
R = cov(randn(1,N));%观测噪声协方差
for k = 2 : N
x(k) = x(k - 1);%预估计 k 时刻状态变量的值
p = p + Q;%对应于预估值的协方差
kg = p / (p + R);%kalman gain
x(k) = x(k) + kg * (y(k) - x(k));
p = (1 - kg) * p;
end
%%%%%%%%%%%Smoothness Filter%%%%%%%%%%%%%%%%%%%%%%%%
Filter_Wid = 10;
smooth_res = zeros(1,N);
for i = Filter_Wid + 1 : N
tempsum = 0;
for j = i - Filter_Wid : i - 1
tempsum = tempsum + y(j);
end
smooth_res(i) = tempsum / Filter_Wid;
end
% figure(1);
% hist(y);
t=1:N;
figure(1);
expValue = zeros(1,N);
for i = 1: N
expValue(i) = CON;
end
plot(t,expValue,'r',t,x,'g',t,y,'b',t,smooth_res,'k');
legend('expected','estimate','measure','smooth result');