Pipelined implementation of high speed STAR-RLS adaptive filters

Kalavai J. Raghunath, Keshab K. Parhi

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

The popular QR decomposition based recursive least-squares (RLS) adaptive filtering algorithm (referred to as QRD-RLS) has a limited speed of operation depending on the processing time of each individual cell. A new scaled tangent rotation based STAR-RLS algorithm has been designed which is suitable for fine-grain pipelining and also has a lower complexity. The inter-cell communication is also reduced by about half. A direct application of look-ahead to STAR-RLS can still lead to some increase in hardware. In this paper look-ahead is applied using delayed update operations such that the complexity is reduced while maintaining a fast convergence. The pipelined STAR-RLS (or PSTAR-RLS) algorithm requires the same number of operations (multiplications or divisions) as the serial STAR-RLS algorithm. Practical issues related to the STAR-RLS algorithm such as numerical stability and dynamic range are also examined.

Original languageEnglish (US)
Pages (from-to)122-133
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume2027
DOIs
StatePublished - Nov 1 1993
EventAdvanced Signal Processing Algorithms, Architectures, and Implementations IV 1993 - San Diego, United States
Duration: Jul 11 1993Jul 16 1993

Bibliographical note

Funding Information:
'This research was supported by the office of Naval Research under contract number N00014-91-J-1008.

Fingerprint Dive into the research topics of 'Pipelined implementation of high speed STAR-RLS adaptive filters'. Together they form a unique fingerprint.

Cite this