粒子滤波matlab代码

以下是一个基于Matlab的粒子滤波算法的代码示例：

function [x_hat, particles_filt] = particle_filter(y, u, particles, w, Q, R)
% y: observation sequence
% u: input sequence
% particles: initial particle set
% w: initial particle weights
% Q: process noise covariance
% R: measurement noise covariance

[N, M] = size(particles); % N: number of particles, M: state dimension
x_hat = zeros(size(particles(:,1))); % initialize state estimate
particles_filt = zeros(size(particles)); % initialize filtered particle set
w_filt = zeros(size(w)); % initialize filtered particle weights

for k = 1:length(y)
    % propagate particles through the dynamics model
    particles = dynamics_model(particles, u(k), Q);
    
    % compute particle weights
    w = measurement_model(y(k), particles, R, w);
    
    % normalize particle weights
    w = w./sum(w);
    
    % resample particles
    [particles, idx] = resample(particles, w);
    w = w(idx);
    
    % save filtered particle set and weights
    particles_filt(:,k) = particles;
    w_filt(:,k) = w;
    
    % compute state estimate
    x_hat = x_hat + sum(bsxfun(@times, particles, w), 1)';
end

end

其中，`dynamics_model`和`measurement_model`分别为粒子滤波算法中的状态转移和观测模型函数，`resample`为重采样函数。使用该函数时，需要提供观测序列`y`、输入序列`u`、初始粒子集合`particles`、初始粒子权重`w`、过程噪声协方差矩阵`Q`和测量噪声协方差矩阵`R`。函数将返回最终的状态估计值`x_hat`和滤波后的粒子集合`particles_filt`。