A systolic array for recursive least squares computations: mapping directionally weighted RLS on an SVD updating array

Author

Moonen, Marc

Author_Institution

ESAT, Katholieke Univ., Leuven, Heverlee, Belgium

Volume

44

Issue

8

fYear

1996

fDate

8/1/1996 12:00:00 AM

Firstpage

2117

Lastpage

2121

Abstract

A systolic algorithm/array is described for recursive least squares (RLS) estimation, which achieves an O(n⁰) throughput rate with O(n²) parallelism. The array is also useful for several other applications, such as, e.g., SVD updating and Kalman filtering. An additional advantage is that unlike with other RLS-arrays, it is now possible to incorporate alternative data weighting strategies, such as directional weighting, without sacrificing speed

Keywords

Kalman filters; filtering theory; least squares approximations; parallel algorithms; recursive estimation; singular value decomposition; systolic arrays; Kalman filtering; SVD updating array; data weighting; directionally weighted RLS; parallelism; recursive least squares computations; recursive least squares estimation; systolic algorithm; systolic array; throughput rate; Concurrent computing; Filtering; Kalman filters; Least squares approximation; Least squares methods; Parallel processing; Resonance light scattering; Signal processing algorithms; Systolic arrays; Throughput;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.533737

Filename

533737