Block majorization-minimization algorithms for low-rank clutter subspace estimation

A. Breloy, Y. Sun, P. Babu, D. P. Palomar

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

Abstract

This paper addresses the problem of the clutter subspace projector estimation in the context of a disturbance composed of a low rank heterogeneous (Compound Gaussian) clutter and white Gaussian noise. We derive two algorithms based on the block majorization-minimization framework to reach the maximum likelihood estimator of the considered model. These algorithms are shown to be computationally faster than the state of the art, with guaranteed convergence. Finally, the performance of the related estimators is illustrated in terms of estimation accuracy and computation speed.

Original languageEnglish (US)
Title of host publication2016 24th European Signal Processing Conference, EUSIPCO 2016
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages2186-2190
Number of pages5
ISBN (Electronic)9780992862657
DOIs
StatePublished - Nov 28 2016
Event24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary
Duration: Aug 28 2016Sep 2 2016

Publication series

NameEuropean Signal Processing Conference
Volume2016-November
ISSN (Print)2219-5491

Conference

Conference24th European Signal Processing Conference, EUSIPCO 2016
CountryHungary
CityBudapest
Period8/28/169/2/16

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Block majorization-minimization algorithms for low-rank clutter subspace estimation'. Together they form a unique fingerprint.

Cite this