Background noise suppression for signal enhancement by novelty filtering

Hanseok Ko, M. Arozullah

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

The enhancement of weak signals in the presence of background and channel noise is necessary to design a robust automatic signal detection and recognition system. The autoassociative property of neural networks can be used to map the identifying characteristics of input source waveforms or their spectra. This paper is directed at the exploitation of such neural network properties for novelty filtering that improves the detection probability of weak signals by learning and subsequent subtraction of noise background from the input waveform. A neural-network-based preprocessor that learns to selectively filter out the background noise without significantly affecting the signal will be highly useful in solving practical signal enhancement problems. An analytical basis is established for the operation of neural-network-based novelty filters that enhance the signal detectability in the presence of noise background and channel noise.

Original languageEnglish
Pages (from-to)102-113
Number of pages12
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume36
Issue number1
DOIs
Publication statusPublished - 2000 Jan 1

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Neural networks
Signal detection

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Aerospace Engineering
  • Electrical and Electronic Engineering

Cite this

Background noise suppression for signal enhancement by novelty filtering. / Ko, Hanseok; Arozullah, M.

In: IEEE Transactions on Aerospace and Electronic Systems, Vol. 36, No. 1, 01.01.2000, p. 102-113.

Research output: Contribution to journalArticle

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