Multi-channel signal detection using time-varying estimation techniques

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

We propose to improve the performance of a generalized coherence (GC) detector using noise-suppressed estimates obtained from time varying (TV) techniques. In source detection and localization, the presence of a common but unknown signal must be detected using data from several noisy channels. If one of the channel outputs has a sufficiently high SNR, then it could be pre-processed to improve detector performance. This work uses matching pursuit decomposition (MPD), and instantaneous frequency (IF) estimation to form an estimate of the highest SNR channel output. The signal estimate is then processed by a GC detector or a generalized likelihood ratio test (GLRT) detector to detect the presence of the signal on the remaining, noisier channels. Detector performance is shown to be significantly improved via simulations.

Original languageEnglish (US)
Title of host publication6th International Symposium on Signal Processing and Its Applications, ISSPA 2001 - Proceedings; 6 Tutorials in Communications, Image Processing and Signal Analysis
PublisherIEEE Computer Society
Pages577-580
Number of pages4
ISBN (Print)0780367030, 9780780367036
DOIs
StatePublished - Jan 1 2001
Event6th International Symposium on Signal Processing and Its Applications, ISSPA 2001 - Kuala Lumpur, Malaysia
Duration: Aug 13 2001Aug 16 2001

Publication series

Name6th International Symposium on Signal Processing and Its Applications, ISSPA 2001 - Proceedings; 6 Tutorials in Communications, Image Processing and Signal Analysis
Volume2

Other

Other6th International Symposium on Signal Processing and Its Applications, ISSPA 2001
Country/TerritoryMalaysia
CityKuala Lumpur
Period8/13/018/16/01

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing

Fingerprint

Dive into the research topics of 'Multi-channel signal detection using time-varying estimation techniques'. Together they form a unique fingerprint.

Cite this