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A Compound Statistical Model Based Radar HRRP Target Recognition

  • Lan Du
  • Hongwei Liu
  • Zheng Bao
  • Junying Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3497)

Abstract

In radar HRRP based statistical target recognition, one of the most challenging tasks is how to accurately describe HRRP’s statistical characteristics. Based on the scattering center model, range resolution cells are classified, in accordance with the number of predominant scatterers in each cell, into three statistical types. In order to model echoes of different types of resolution cells as the corresponding distribution forms, this paper develops a compound statistical model comprising two distribution forms, i.e. Gamma distribution and Gaussian mixture distribution, for target HRRP. Determination of the type of a resolution cell is achieved by using the rival penalized competitive learning (RPCL) algorithm. In the recognition experiments based on measured data, the proposed compound model not only has better recognition performance but also is more robust to noises than the two existing statistical models, i.e. Gaussian model and Gamma model.

Keywords

Gaussian Model Synthetic Aperture Radar Image Distribution Form Inverse Synthetic Aperture Radar Gamma Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Lan Du
    • 1
  • Hongwei Liu
    • 1
  • Zheng Bao
    • 1
  • Junying Zhang
    • 1
  1. 1.National Lab. of Radar Signal ProcessingXidian UniversityXi’anChina

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