CKF_UPDATE - Cubature Kalman filter update step Syntax: [M,P,K,MU,S,LH] = CKF_UPDATE(M,P,Y,h,R,param) 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. 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 spherical-radial cubature Kalman filter (CKF) measurement update step. Assumes additive measurement noise. Function h should be such that it can be given 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] = ckf_update(M1,P1,Y,h,R,S); See also: CKF_PREDICT, CRTS_SMOOTH, CKF_TRANSFORM, SPHERICALRADIAL References: Arasaratnam and Haykin (2009). Cubature Kalman Filters. IEEE Transactions on Automatic Control, vol. 54, no. 5, pp.1254-1269

- ckf_transform CKF_TRANSFORM - Cubature Kalman filter transform of random variables
- gauss_pdf GAUSS_PDF Multivariate Gaussian PDF

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