Denoising multisensor data

Anil M. Rao, Douglas L Jones

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Multisensor array processing of noisy measurements has received considerable attention in many areas of signal processing. The optimal processing techniques developed so far usually assume the signal and noise processes are at least wide-sense-stationary, yet a need exists for efficient, effective methods for processing nonstationary signals. 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 multisensor setting has only recently been considered. In this work we show how multisensor denoising can be carried out in perturbed, narrowband arrays even in the absence of the signal source's direction of arrival. We show that our proposed blind estimator can be implemented efficiently and robustly employing only wavelet and discrete Fourier transforms while entailing only a small loss in performance.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
Pages375-379
Number of pages5
ISBN (Electronic)0780363396
DOIs
StatePublished - Jan 1 2000

Keywords

  • Array signal processing
  • Discrete Fourier transforms
  • Discrete wavelet transforms
  • Estimation
  • Narrowband
  • Noise reduction
  • Robustness
  • Sensor arrays
  • Signal processing
  • Signal processing algorithms

ASJC Scopus subject areas

  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Rao, A. M., & Jones, D. L. (2000). Denoising multisensor data. In Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop (pp. 375-379). [878033] https://doi.org/10.1109/SAM.2000.878033