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/*
* Purpose: use SRIF Factorization Matrix Update Time
* ------------------------------------------------------------
* Example:
* | Rwk_1 0 0 | QR -> | Rwk Rwx Zw |
* | -Rp*Phi_Inv*G Rp*Phi_Inv Zp| | 0 Rp Zp |
* [Rd Zd; 0 ed] stored in AL. AL as input,meanwhile as output
*-------------------------------------------------------------
* Input:
* Rp: a priori square root information (SRI) matrix (an n * n upper triangular matrix)
* Zp: a priori SRIF state vector, of length n*1 (state is X, Zp = Rp*X).
* Phi: transition matrix, an n * n matrix.
* G : The n by ns matrix associated with process noise.
* The process noise covariance is G*Q*transpose(G) where inverse(Q)
* is transpose(Rw)*Rw. G is destroyed on output.
* Rwk_1: a priori square root information matrix for the process noise, an ns by ns upper triangular matrix
* Zw : a priori 'state' associated with the process noise, a vector with ns elements. Usually set to zero by
* the calling routine (for unbiased process noise).
* Rw: An ns by n matrix which is set to zero by this routine, but is used for output.
* -------------------------------------------------------------
* output:
* Rp: updated matrix (upper triangular, dimension N*N)
* Zp: updated vector (length N)
* Rwk_1: a posteriori square root information matrix for the process noise, an ns by ns upper triangular matrix
* Rwx:
* Zw :
* [Rwk_1 Rwx Zw] use to SRIF smoothing data
* where Rp*x = Zp
* -------------------------------------------------------------
* Authors: XiaoGongWei; Email: [email protected];
* Date: 2018-10-17; github: https://github.com/xiaogongwei
* -------------------------------------------------------------
* reference: Bierman, G.J. "Factorization Methods for Discrete Sequential
* Estimation," Academic Press, 1977.
*/
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