ex11.m

本程序是基于机动目标跟踪课题的整个算法程序

function Particle

% Particle filter 

x = 0.1; % 初始状态
Q = 1; % 过程噪声协方差
R = 1; % 测量噪声协方差
tf = 50; % 仿真长度

N = 100; % 粒子滤波器粒子数

xhat = x;
P = 2;
xhatPart = x;

% 初始化粒子过滤器
for i = 1 : N
    xpart(i) = x + sqrt(P) * randn;
end

xArr = [x];
yArr = [x^2 / 20 + sqrt(R) * randn];
xhatArr = [x];
PArr = [P];
xhatPartArr = [xhatPart];

close all;

for k = 1 : tf
    % 系统仿真
    x = 0.5 * x + 25 * x / (1 + x^2) + 8 * cos(1.2*(k-1)) + sqrt(Q) * randn;%状态方程
    y = x^2 / 20 + sqrt(R) * randn;%观测方程
   
    for i = 1 : N
        xpartminus(i) = 0.5 * xpart(i) + 25 * xpart(i) / (1 + xpart(i)^2) + 8 * cos(1.2*(k-1)) + sqrt(Q) * randn;
        ypart = xpartminus(i)^2 / 20;
        vhat = y - ypart;%观测和预测的差
        q(i) = (1 / sqrt(R) / sqrt(2*pi)) * exp(-vhat^2 / 2 / R);
    end
    %正常化的可能性，每个先验估计
    qsum = sum(q);
    for i = 1 : N
        q(i) = q(i) / qsum;%归一化权重
    end
    % 重采样
    for i = 1 : N
        u = rand; % 均匀随机数介于0和1
        qtempsum = 0;
        for j = 1 : N
            qtempsum = qtempsum + q(j);
            if qtempsum >= u
                xpart(i) = xpartminus(j);
                break;
            end
        end
    end
    
    xhatPart = mean(xpart);
    % Plot the estimated pdf's at a specific time.
    if k == 20
        % Particle filter pdf
        pdf = zeros(81,1);
        for m = -40 : 40
            for i = 1 : N
                if (m <= xpart(i)) && (xpart(i) < m+1)
                    pdf(m+41) = pdf(m+41) + 1;
                end
            end
        end
        
        m = -40 : 40; 
        pdf = (1 / sqrt(P) / sqrt(2*pi)) .* exp(-(m - xhat).^2 / 2 / P);
    end
    xArr = [xArr x];
    yArr = [yArr y];
    xhatArr = [xhatArr xhat];
    PArr = [PArr P];
    xhatPartArr = [xhatPartArr xhatPart];
end

t = 0 : tf;
figure;
plot(t, xArr, 'b.', t, xhatPartArr, 'k-');
set(gca,'FontSize',12); set(gcf,'Color','White'); 
xlabel('time step'); ylabel('state');
legend('True state', 'Particle filter estimate');