Nonstationary Hidden Markov Models for Multiaspect Discriminative Feature Extraction From Radar Targets

Author

Zhu, Feng ; Zhang, Xian-Da ; Hu, Ya-Feng ; Xie, Deguang

Author_Institution

Dept. of Autom., Tsinghua Univ., Beijing

Volume

55

Issue

5

fYear

2007

fDate

5/1/2007 12:00:00 AM

Firstpage

2203

Lastpage

2214

Abstract

This paper presents a new scheme for radar target recognition, in which we fuse sequential radar echoes from multiple target-radar aspect angles. The nonstationary hidden Markov model (NSHMM) is employed to characterize the sequential information contained in multiaspect radar echoes. Features from echoes are extracted via the multirelax algorithm, and moments are used to reduce the extracted-feature dimensionality. The proposed NSHMM has many parameters and states to be estimated, so the Markov chain Monte Carlo sampling algorithm is adopted. Finally, this new scheme is demonstrated with experiments on inverse synthetic aperture radar data

Keywords

Monte Carlo methods; feature extraction; hidden Markov models; radar cross-sections; radar target recognition; Markov chain Monte Carlo sampling algorithm; extracted-feature dimensionality; multiaspect discriminative feature extraction; multiple target-radar aspect angles; multirelax algorithm; nonstationary hidden Markov models; radar target recognition; sequential radar echoes; Data mining; Feature extraction; Fuses; Hidden Markov models; Information science; Inverse synthetic aperture radar; Monte Carlo methods; Radar applications; State estimation; Target recognition; Feature extraction; Markov chain Monte Carlo (MCMC); high-range resolution profile (HRRP); nonstationary hidden Markov model (NSHMM); radar target recognition;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/TSP.2007.892708

Filename

4156439