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
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;
Conference_Titel :
Radar Conference (RADAR), 2011 IEEE
Conference_Location :
Kansas City, MO
Print_ISBN :
978-1-4244-8901-5
DOI :
10.1109/RADAR.2011.5960631