Denoising approach to multichannel signal estimation

Anil M. Rao, Douglas L. Jones

Research output: Contribution to journalConference article

Abstract

Multichannel sensor array processing has received considerable attention in many important areas of signal processing. Almost all data recorded by multisensor instruments contain various amounts of noise, and much work has been done in developing optimal processing structures for estimating the signal source from the noisy multichannel observations. The techniques developed so far assume the signal and noise processes are at least wide-sense-stationary so that optimal linear estimation can be achieved with a set of linear, time-invariant filters. Unfortunately, nonstationary signals arise in many important applications and there is no efficient structure with which to optimally deal with them. While wavelets have proven to be useful tools in dealing with certain nonstationary signals, the way in which wavelets are to be used in the multichannel setting is still an open question. Based on the structure for optimal linear estimation of nonstationary multichannel data and statistical models of spatial signal coherence, we propose a method to obtain an efficient multichannel estimator based on the wavelet transform.

Original languageEnglish (US)
Pages (from-to)2869-2872
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume5
StatePublished - Jan 1 1999
EventProceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99) - Phoenix, AZ, USA
Duration: Mar 15 1999Mar 19 1999

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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