基于t分布扩展概率主成分分析模型的一维距离像识别方法

doi:10.11999/JEIT161220

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Abstract

In order to improve the sensitivity problem of using Probabilistic Principal Component Analysis (PPCA) model for HRRP recognition, a modified method is proposed. T-distribution is adopted as the basis of PPCA model rather than Gaussian distribution in this method, which utilizes not only the t-distribution’s robustness, but also less free parameters of PPCA characteristic. Further, to eliminate the targets’ azimuth sensitivity, the mixture t-distribution is substituted for single t-distribution. This modification offers a potential to model the similar density of HRRP in different azimuth range adequately for clustering and reduces the mismatch between models, thus improves the recognition performance. Estimation of parameters is achieved by EM algorithm to avoid the drawbacks of maximum-likelihood estimation and improve the estimation efficiency. Finally, in the simulation experiment Bayesian rule and the estimation statistical features are adopted together to test new HRRPs, the results show this method can improve the robustness of PPCA model in low SNR conditions.

Key words： Radar target recognition High Resolution Range Profile (HRRP) Probabilistic Principal Component Analysis (PPCA) t-distribution Feature extraction

Received: 10 November 2016 Published: 11 May 2017

PACS:

TN 957.51

Fund:

The National Natural Science Foundation of China (61571364), The Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Poly-technical University (Z2017022)

Corresponding Authors: LI Bin E-mail: libin.1980@163.com

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	LI Bin
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	GUO Songyun

Cite this article:

LI Bin,LI Hui,GUO Songyun. Using t-distribution Based Probabilistic Principal Component Analysis Model for High Resolution Range Profile Recognition[J]. JEIT, 2017, 39(8): 1857-1864.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT161220 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I8/1857

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