Efficient L-D factorization algorithms for PDA, IMM, and IMMPDA filters

Author

Raghavan, Vijaya ; Pattipati, Krishna R. ; Bar-shalom, Yaakov

Author_Institution

Dept. of Electr. & Syst. Eng., Connecticut Univ., Storrs, CT, USA

Volume

29

Issue

4

fYear

1993

fDate

10/1/1993 12:00:00 AM

Firstpage

1297

Lastpage

1310

Abstract

Efficient algorithms exist for the square-root probabilistic data association filter (PDAF). The same approach is extended to develop square-root versions of the interacting multiple model (IMM) Kalman filter and the IMMPDAF algorithms. The computational efficiency of the method stems from the fact that the terms needed in the overall covariance updates of PDAF, IMM, and IMMPDAF can be obtained as part of the square-root covariance update of an ordinary Kalman filter. In addition, a new square-root covariance prediction algorithm that is substantially faster than the usual modified weighted Gram-Schmidt (MWG-S) algorithm, whenever the process noise covariance matrix is time invariant, is proposed

Keywords

Kalman filters; filtering and prediction theory; matrix algebra; probability; Kalman filter; L-D factorization algorithms; interacting multiple model; noise covariance matrix; square-root covariance prediction algorithm; square-root probabilistic data association filter; Computational efficiency; Computer errors; Covariance matrix; Error correction; Filtering algorithms; Kalman filters; Noise robustness; Personal digital assistants; Roundoff errors; Target tracking;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.259533

Filename

259533