New square-root smoothing algorithms

Author

Park, PooGyeon ; Kailath, Thomas

Author_Institution

Inf. Syst. Lab., Stanford Univ., CA, USA

Volume

2

fYear

1994

Firstpage

1215

Abstract

New square-root smoothing algorithms are suggested for the four kinds of smoothing formulas: (1) Bryson-Frazier (BF) formulas, (2) Rauch-Tung-Striebel (RTS) formulas, (3) backward RTS formulas and (4) two-filter formulas. These algorithms are compared on the basis of constraints, speed in real-time or batch processing, array size, memory size, and so on. The main features of the new algorithms are to implement all the formulas with square-root arrays composed of the filtered or smoothed estimates and their error covariances, and to avoid inversion or backwards substitution in all the formulas: these features provide many advantages over the conventional algorithms with respect to systolic array and parallel implementations as well as numerical stability and conditioning

Keywords

Kalman filters; numerical stability; parallel algorithms; recursive estimation; smoothing methods; state-space methods; Bryson-Frazier formulas; array size; backward Rauch-Tung-Striebel formulas; batch processing; conditioning; constraints; error covariances; memory size; numerical stability; square-root smoothing algorithms; two-filter formulas; Equations; Information filtering; Information filters; Information systems; Numerical stability; Prediction algorithms; Smoothing methods; State estimation; Systolic arrays; Time measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on

Conference_Location

Lake Buena Vista, FL

Print_ISBN

0-7803-1968-0

Type

conf

DOI

10.1109/CDC.1994.411165

Filename

411165