Micro-doppler parameter estimation via parametric sparse representation and pruned orthogonal matching pursuit

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62 Scopus citations

Abstract

The rotation, vibration, or coning motion of a target may produce periodic Doppler modulation, which is called the micro-Doppler phenomenon and is widely used for target classification and recognition. In this paper, the signal of interest is decomposed into a family of parametric basis-signals that are generated by discretizing the micro-Doppler parameter domain and synthesizing the micro-Doppler components with over-complete time-frequency characteristics. In this manner, micro-Doppler parameter estimation is converted into the problem of sparse signal recovery with a parametric dictionary. This problem can be considered as a specific case of dictionary learning, i.e., we need to solve for both the sparse solution and the parameter inside the dictionary matrix. To solve this problem, a novel pruned orthogonal matching pursuit (POMP) algorithm is proposed, in which the pruning operation is embedded into the iterative process of the orthogonal matching pursuit (OMP) algorithm. The effectiveness of the proposed approach is validated by simulations.

Original languageEnglish (US)
Article number6810175
Pages (from-to)4937-4948
Number of pages12
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume7
Issue number12
DOIs
StatePublished - Dec 1 2014

Keywords

  • Compressed sensing (CS)
  • Micro-Doppler
  • Parametric sparse representation
  • Time-frequency analysis

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science

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