Moving Target Detection in Distributed MIMO Radar on Moving Platforms

Hongbin Li, Zhe Wang, Jun Liu, Braham Himed

Research output: Contribution to journalArticlepeer-review

79 Scopus citations

Abstract

This paper examines moving target detection in distributed multi-input multi-output radar with sensors placed on moving platforms. Unlike previous works which were focused on stationary platforms, we consider explicitly the effects of platform motion, which exacerbate the location-induced clutter non-homogeneity inherent in such systems and thus make the problem significantly more challenging. Two new detectors are proposed. The first is a sparsity based detector which, by exploiting a sparse representation of the clutter in the Doppler domain, adaptively estimates from the test signal the clutter subspace, which is in general distinct for different transmit/receive pairs and, moreover, may spread over the entire Doppler bandwidth. The second is a fully adaptive parametric detector which employs a parametric autoregressive clutter model and offers joint model order selection, clutter estimation/mitigation, and target detection in an integrated and fully adaptive process. Both detectors are developed within the generalized likelihood ratio test (GLRT) framework, obviating the need for training signals that are indispensable for conventional detectors but are difficult to obtain in practice due to clutter non-homogeneity. Numerical results indicate that the proposed training-free detectors offer improved detection performance over covariance matrix based detectors when the latter have a moderate amount of training signals.

Original languageEnglish
Article number7185379
Pages (from-to)1524-1535
Number of pages12
JournalIEEE Journal on Selected Topics in Signal Processing
Volume9
Issue number8
DOIs
StatePublished - Dec 2015

Keywords

  • Distributed multi-input multi-output (MIMO) radar
  • moving platforms
  • moving target detection
  • parametric methods
  • sparsity

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