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:

ekf2\ekf\ekf.m
....\ekf.m
....\ekf
ekf2

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