DocumentCode :
2385459
Title :
Radar HRRP target recognition in frequency domain based on autoregressive model
Author :
Wang, Penghui ; Dai, Fengzhou ; Pan, Mian ; Du, Lan ; Liu, Hongwei
Author_Institution :
Nat. Lab. of Radar Signal Process., Xidian Univ., Xi´´an, China
fYear :
2011
fDate :
23-27 May 2011
Firstpage :
714
Lastpage :
717
Abstract :
In this paper, we adopt the autoregressive (AR) model to characterize the frequency spectrum amplitude of high resolution range profile (HRRP) and extract the AR and partial correlation (PARCOR) coefficients, which are invariant to the initial-phase, translation and scale changes of HRRP, as discriminating features. Moreover, a mixture model based frame partition method is proposed and a Bayesian Ying-Yang (BYY) harmony learning algorithm is adopted to determine the frame number automatically during parameter learning. Experimental results based on measured data demonstrate the proposed features are superior to others in their minor frame number, robustness to sample size and good rejection ability.
Keywords :
Bayes methods; autoregressive processes; object detection; radar detection; Bayesian Ying-Yang harmony learning algorithm; autoregressive model; frequency domain; high resolution range profile; mixture model based frame partition method; parameter learning; partial correlation coefficients; radar HRRP target recognition; Bayesian methods; Feature extraction; Radar; Radar signal processing; Sensitivity; Target recognition; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Radar Conference (RADAR), 2011 IEEE
Conference_Location :
Kansas City, MO
ISSN :
1097-5659
Print_ISBN :
978-1-4244-8901-5
Type :
conf
DOI :
10.1109/RADAR.2011.5960631
Filename :
5960631
Link To Document :
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