Matlab关于粒子滤波代码与卡尔曼做比较-untitled3.fig
部分程序如下：
function ParticleEx1
% Particle filter example, adapted from Gordon, Salmond, and Smith paper.
x = 0.1; % initial state
Q = 1; % process noise covariance
R = 1; % measurement noise covariance
tf = 50; % simulation length
N = 100; % number of particles in the particle filter
xhat = x;
P = 2;
xhatPart = x;
% Initialize the particle filter.
for i = 1 : N
xpart = x sqrt * randn;
end
xArr = [x];
yArr = [x^2 / 20 sqrt * randn];
xhatArr = [x];
PArr = [P];
xhatPartArr = [xhatPart];
close all;
for k = 1 : tf
% System simulation
x = 0.5 * x 25 * x / 8 * cos) sqrt * randn;%状态方程
y = x^2 / 20 sqrt * randn;%观测方程
% Extended Kalman filter
F = 0.5 25 * / ^2;
P = F * P * F' Q;
H = xhat / 10;
K = P * H' * ^;
xhat = 0.5 * xhat 25 * xhat / 8 * cos);%预测
xhat = xhat K * ;%更新
P = * P;
% Particle filter
for i = 1 : N
运行结果：
Figure39.jpg
2022-11-21 16:31:41
9KB
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粒子滤波（PF），粒子群优化粒子滤波（PSO-PF）matlab程序，整合程序，增加注释并用子程序编写，适用于初学者根据自己研究内容改写。
2022-11-04 14:05:48
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粒子滤波算法预测电池寿命。包括电池容量数据和MATLAB程序。
2022-10-22 22:48:50
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采用二阶RC模型，需要代入自己的数据的地方：
R0 = @(x)(-0.07495*(x(4))^4+0.2187*(x(4))^3-0.1729*(x(4))^2+0.01904*(x(4))+0.1973);
R1 = @(x)(0.07826*(x(4))^4-0.2208*(x(4))^3+0.217*(x(4))^2-0.08761*(x(4))+0.01664);
R2 = @(x)(0.1248*(x(4))^4-0.2545*(x(4))^3+0.1254*(x(4))^2-0.03868*(x(4))+0.05978);
C1 = @(x)(2431*(x(4))^4-4606*(x(4))^3+3084*(x(4))^2-589*(x(4))+209.8);
C2 = @(x)(681.1*(x(4))^4-3197*(x(4))^3+4595*(x(4))^2-3114*(x(4))+1444);
2022-10-21 20:38:32
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