Title :
New square-root smoothing algorithms
Author :
Park, PooGyeon ; Kailath, Thomas
Author_Institution :
Inf. Syst. Lab., Stanford Univ., CA, USA
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;
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
DOI :
10.1109/CDC.1994.411165
