说明：  一种快速Kalman滤波算法实现,。对于某些不能够采取离线计算的滤波过程来说，它可以在保证一定精度的同时极大地提高计算速度和减少计算占用资源
( EKF Extended Kalman Filter for nonlinear dynamic systems [x, P] = ekf(f,x,P,h,z,Q,R) returns state estimate, x and state covariance, P for nonlinear dynamic system: x_k+1 = f(x_k)+ w_k z_k = h(x_k)+ v_k where w ~ N(0,Q) meaning w is gaussian noise with covariance Q v ~ N(0,R) meaning v is gaussian noise with covariance R Inputs: f: function handle for f(x) x: a priori state estimate P: a priori estimated state covariance h: fanction handle for h(x) z: current measurement Q: process noise covariance R: measurement noise covariance Output: x: a posteriori state estimate P: a posteriori state covariance Example:)