RESEARCH ARTICLE

Radar HRRP statistical recognition with temporal factor analysis by automatic Bayesian Ying-Yang harmony learning

  • Penghui WANG 1 ,
  • Lei SHI 2 ,
  • Lan DU 1 ,
  • Hongwei LIU 1 ,
  • Lei XU , 2 ,
  • Zheng BAO 1
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  • 1. National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
  • 2. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China

Received date: 28 Mar 2011

Accepted date: 15 Apr 2011

Published date: 05 Jun 2011

Copyright

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

Abstract

Radar high-resolution range profiles (HRRPs) are typical high-dimensional and interdimension dependently distributed data, the statistical modeling of which is a challenging task for HRRP-based target recognition. Supposing that HRRP samples are independent and jointly Gaussian distributed, a recent work [Du L, Liu H W, Bao Z. IEEE Transactions on Signal Processing, 2008, 56(5): 1931-1944] applied factor analysis (FA) to model HRRP data with a two-phase approach for model selection, which achieved satisfactory recognition performance. The theoretical analysis and experimental results reveal that there exists high temporal correlation among adjacent HRRPs. This paper is thus motivated to model the spatial and temporal structure of HRRP data simultaneously by employing temporal factor analysis (TFA) model. For a limited size of high-dimensional HRRP data, the two-phase approach for parameter learning and model selection suffers from intensive computation burden and deteriorated evaluation. To tackle these problems, this work adopts the Bayesian Ying-Yang (BYY) harmony learning that has automatic model selection ability during parameter learning. Experimental results show stepwise improved recognition and rejection performances from the two-phase learning based FA, to the two-phase learning based TFA and to the BYY harmony learning based TFA with automatic model selection. In addition, adding many extra free parameters to the classic FA model and thus becoming even worse in identifiability, the model of a general linear dynamical system is even inferior to the classic FA model.

Cite this article

Penghui WANG , Lei SHI , Lan DU , Hongwei LIU , Lei XU , Zheng BAO . Radar HRRP statistical recognition with temporal factor analysis by automatic Bayesian Ying-Yang harmony learning[J]. Frontiers of Electrical and Electronic Engineering, 2011 , 6(2) : 300 -317 . DOI: 10.1007/s11460-011-0149-8

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