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

- gauss_pdf GAUSS_PDF Multivariate Gaussian PDF
- gh_transform GH_TRANSFORM - Gauss-Hermite transform of random variables

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