Abstract

We propose a method for estimating the space-time covariance matrix of rapidly-varying sea clutter following a dynamic state space matrix model. The covariance matrix dimension can become computationally infeasible as it increases with the number of range bins and dwell pulses required for coherent processing. In order to reduce the computational complexity, we apply the Kronecker product (KP) approximation and particle filtering to estimate the space-time covariance matrix, and we demonstrate the proposed method's validity using real clutter data. We also demonstrate that the method ensures that the covariance matrix estimate is always positive definite. The covariance matrix estimation is integrated with a track-before-detect filter for tracking a low radar cross-section (RCS) target in strong sea clutter.

Original languageEnglish (US)
Article number7234866
Pages (from-to)1639-1649
Number of pages11
JournalIEEE Journal on Selected Topics in Signal Processing
Volume9
Issue number8
DOIs
StatePublished - Dec 1 2015

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Radar clutter
Radar cross section
Approximation algorithms
Covariance matrix
Target tracking
Bins
Computational complexity
Processing

Keywords

  • particle filters
  • radar clutter
  • Radar detection
  • radar tracking

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing

Cite this

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title = "Low RCS Target Tracking in Estimated Rapidly Varying Sea Clutter Using a Kronecker Product Approximation Algorithm",
abstract = "We propose a method for estimating the space-time covariance matrix of rapidly-varying sea clutter following a dynamic state space matrix model. The covariance matrix dimension can become computationally infeasible as it increases with the number of range bins and dwell pulses required for coherent processing. In order to reduce the computational complexity, we apply the Kronecker product (KP) approximation and particle filtering to estimate the space-time covariance matrix, and we demonstrate the proposed method's validity using real clutter data. We also demonstrate that the method ensures that the covariance matrix estimate is always positive definite. The covariance matrix estimation is integrated with a track-before-detect filter for tracking a low radar cross-section (RCS) target in strong sea clutter.",
keywords = "particle filters, radar clutter, Radar detection, radar tracking",
author = "Ebenezer, {Samuel P.} and Antonia Papandreou-Suppappola",
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AB - We propose a method for estimating the space-time covariance matrix of rapidly-varying sea clutter following a dynamic state space matrix model. The covariance matrix dimension can become computationally infeasible as it increases with the number of range bins and dwell pulses required for coherent processing. In order to reduce the computational complexity, we apply the Kronecker product (KP) approximation and particle filtering to estimate the space-time covariance matrix, and we demonstrate the proposed method's validity using real clutter data. We also demonstrate that the method ensures that the covariance matrix estimate is always positive definite. The covariance matrix estimation is integrated with a track-before-detect filter for tracking a low radar cross-section (RCS) target in strong sea clutter.

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