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ekf_update1

PURPOSE ^

EKF_UPDATE1 1st order Extended Kalman Filter update step

SYNOPSIS ^

function [M,P,K,MU,S,LH] = ekf_update1(M,P,y,H,R,h,V,param)

DESCRIPTION ^

EKF_UPDATE1  1st order Extended Kalman Filter update step

 Syntax:
   [M,P,K,MU,S,LH] = EKF_UPDATE1(M,P,Y,H,R,[h,V,param])

 In:
   M  - Nx1 mean state estimate after prediction step
   P  - NxN state covariance after prediction step
   Y  - Dx1 measurement vector.
   H  - Derivative of h() with respect to state as matrix,
        inline function, function handle or name
        of function in form H(x,param)
   R  - Measurement noise covariance.
   h  - Mean prediction (innovation) as vector,
        inline function, function handle or name
        of function in form h(x,param).               (optional, default H(x)*X)
   V  - Derivative of h() with respect to noise as matrix,
        inline function, function handle or name
        of function in form V(x,param).               (optional, default identity)
   param - Parameters of h                            (optional, default empty)

 Out:
   M  - Updated state mean
   P  - Updated state covariance
   K  - Computed Kalman gain
   MU - Predictive mean of Y
   S  - Predictive covariance of Y
   LH - Predictive probability (likelihood) of measurement.
   
 Description:
   Extended Kalman Filter measurement update step.
   EKF model is

     y[k] = h(x[k],r),   r ~ N(0,R)

 See also:
   EKF_PREDICT1, EKF_PREDICT2, EKF_UPDATE2, DER_CHECK,
   LTI_DISC, KF_UPDATE, KF_PREDICT

CROSS-REFERENCE INFORMATION ^

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