System identification and filtering using pseudo random binary inputs

Tamal Bose, Somenath Mitra

Research output: Contribution to journalArticle

3 Citations (Scopus)

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 - 1992
Externally publishedYes

Fingerprint

Impulse Response
System Identification
Impulse response
Pseudorandom Sequence
Binary sequences
Identification (control systems)
Filtering
Binary Sequences
Filter
Binary
Post-processing
Least Mean Square
Least Square Algorithm
Recursive Algorithm
Processing
System Modeling
Baseline
Unknown
System identification

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems and Management
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Control and Optimization
  • Modeling and Simulation

Cite this

System identification and filtering using pseudo random binary inputs. / Bose, Tamal; Mitra, Somenath.

In: Journal of the Franklin Institute, Vol. 329, No. 4, 1992, p. 765-774.

Research output: Contribution to journalArticle

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