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