Adaptive array processing in non-Gaussian environments

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations

Abstract

In several adaptive array application areas the Gaussian distribution has not proven to be an accurate model of the measured data. Nevertheless, Gaussian based processors have demonstrated robust performance in spite of this statistical mismatch. A need therefore exists for the consideration of (i) problem reformulation and (ii) performance analysis in non-Gaussian environments. The theory of complex multivariate elliptically contoured (MEC) distributions provides an attractive theoretic framework for these considerations especially in the adaptive array setting. We replace the Gaussian data assumption with one of MEC distributed and reexamine the optimality and performance of widely used adaptive detection and beamforming structures.

Original languageEnglish (US)
Pages562-565
Number of pages4
StatePublished - Jan 1 1996
Externally publishedYes
EventProceedings of the 1996 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing, SSAP'96 - Corfu, Greece
Duration: Jun 24 1996Jun 26 1996

Other

OtherProceedings of the 1996 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing, SSAP'96
CityCorfu, Greece
Period6/24/966/26/96

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Adaptive array processing in non-Gaussian environments'. Together they form a unique fingerprint.

Cite this