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ghkf_update

PURPOSE ^

GHKF_UPDATE - Gauss-Hermite Kalman filter update step

SYNOPSIS ^

function [M,P,K,MU,S,LH] = ghkf_update(M,P,Y,h,R,h_param,p)

DESCRIPTION ^

 GHKF_UPDATE - Gauss-Hermite Kalman filter update step

 Syntax:
   [M,P,K,MU,S,LH] = GHKF_UPDATE(M,P,Y,h,R,param,p)

 In:
   M  - Mean state estimate after prediction step
   P  - State covariance after prediction step
   Y  - Measurement vector.
   h  - Measurement model function as a matrix H defining
        linear function h(x) = H*x, inline function,
        function handle or name of function in
        form h(x,param)
   R  - Measurement covariance
   h_param - Parameters of h
   p  - Degree of approximation (number of quadrature points)

 Out:
   M  - Updated state mean
   P  - Updated state covariance
   K  - Computed Kalman gain
   MU - Predictive mean of Y
   S  - Predictive covariance Y
   LH - Predictive probability (likelihood) of measurement.
   
 Description:
   Perform additive form Gauss-Hermite Kalman filter (GHKF)
   measurement update step. Assumes additive measurement
   noise.

   Function h(.) should be such that it can be given a
   DxN matrix of N sigma Dx1 points and it returns 
   the corresponding measurements for each sigma
   point. This function should also make sure that
   the returned sigma points are compatible such that
   there are no 2pi jumps in angles etc.

 Example:
   h = inline('atan2(x(2,:)-s(2),x(1,:)-s(1))','x','s');
   [M2,P2] = ghkf_update(M1,P1,Y,h,R,S);

 See also:
   GHKF_PREDICT, GHRTS_SMOOTH, GH_TRANSFORM

CROSS-REFERENCE INFORMATION ^

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