Factorization Methods for Discrete Sequential Estimation
This estimation reference text thoroughly describes matrix factorization methods successfully employed by numerical analysts, familiarizing readers with the techniques that lead to efficient, economical, reliable, and flexible estimation algorithms. Geared toward advanced undergraduates and graduate students, this pragmatically oriented presentation is also a useful reference, featuring numerous appendixes. 1977 edition.
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applications augmented bias parameters Bierman Chapter Cholesky colored noise column component computed covariance computer implementation computer storage consider covariance consider filter consider parameters conventional Kalman corresponding covariance matrix covariance square root data equation data processing algorithm developed diagonal effects efficient elements error analysis error covariance estimate and covariance estimation theory filter algorithm filter covariance filter estimate formulae FORTRAN mechanization Householder transformations included incorrect a priori information array information matrix involve Kalman algorithm Kalman filter Kalman gain KrKT linear lower triangular mapping measurement method mismodeling normal equations notation numeric stability observation obtain operation counts orthogonal matrix orthogonal transformation partitioning Potter priori covariance priori estimate priori statistics propagation pseudoinverse recursion Remark result scalar Section smoothed estimate square root algorithms square root covariance square root information SRIF storage requirements subscript tion triangular factorization triangular square U—D factorization upper triangular matrix variables vector xTPx