Abstract

In this paper, we propose a method to estimate the space-time covariance matrix of rapidly varying sea clutter. The method first develops a dynamic state space representation for the covariance matrix and then approximates the covariance using the nearest Kronecker product to reduce computational complexity. Particle filtering is then applied to estimate the dynamic elements of the covariance matrix. We validate the nearest Kronecker product approximation using real sea clutter radar measurements. We further demonstrate the use of the estimated space-time covariance matrix in the track-before-detect filter to track a low observable target in sea clutter.

Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3686-3690
Number of pages5
Volume2015-August
ISBN (Print)9781467369978
DOIs
StatePublished - Aug 4 2015
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: Apr 19 2014Apr 24 2014

Other

Other40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
CountryAustralia
CityBrisbane
Period4/19/144/24/14

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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