System identification and filtering using pseudo random binary inputs

Tamal Bose, Somenath Mitra

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)765-774
Number of pages10
JournalJournal of the Franklin Institute
Volume329
Issue number4
DOIs
StatePublished - Jan 1 1992

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Applied Mathematics

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