System identification and filtering using pseudo random binary inputs

Document Type

Article

Publication Date

1-1-1992

Abstract

The problem of identifying the impulse response of an unknown system is investigated when the input is restricted to a pseudo random binary sequence (PRBS). The well-known methods, such as the Least Mean Square (LMS) and the Recursive Least Square (RLS) algorithms, are studied for system modeling with a PRBS input and the results are compared. It is shown that post-processing the impulse response with a suitable filter may lead to even better identification. A new post-processing filter, namely, the Med-Mean (MM) filter, is proposed which smoothes the baseline noise of the identified impulse response while preserving the edges. © 1992.

Identifier

38249011879 (Scopus)

Publication Title

Journal of the Franklin Institute

External Full Text Location

https://doi.org/10.1016/0016-0032(92)90087-W

ISSN

00160032

First Page

765

Last Page

774

Issue

4

Volume

329

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