用matlab写扩展卡尔曼滤波

可以直接回答该问题，以下是扩展卡尔曼滤波的MATLAB实现示例代码：

function [xhat, Phat] = EKF(fstate, x, hmeas, z, P, Q, R)
% fstate : function handle for f(x)
% x : a priori state estimate
% hmeas : function handle for h(x)
% z : measurement vector
% P : a priori error covariance
% Q : process noise covariance
% R : measurement noise covariance

%-- Prediction step --
[xpred, Jf] = jacobianest(fstate, x); % Jacobian of f(x)
Ppred = Jf * P * Jf' + Q; % Covariance of prediction

%-- Update step --
[hx, Jh] = jacobianest(hmeas, xpred); % Jacobian of h(x)
K = Ppred * Jh' / (Jh * Ppred * Jh' + R); % Kalman gain
xhat = xpred + K * (z - hx); % a posteriori state estimate
Phat = (eye(size(P)) - K * Jh) * Ppred; % Covariance of estimate

end

其中，fstate和hmeas分别是系统的状态转移方程和观测方程的函数句柄。Jacobianest函数用于估计函数的雅可比矩阵，x是状态的先验估计，P是先验误差协方差，Q和R分别是过程噪声协方差和测量噪声协方差。返回值xhat和Phat分别是状态的后验估计和误差协方差。