High Range Resolution Profile Automatic Target Recognition Using Sparse Representation

Abstract Sparse representation is a new signal analysis method which is receiving increasing attention in recent years. In this article, a novel scheme solving high range resolution profile automatic target recognition for ground moving targets is proposed. The sparse representation theory is applied to analyzing the components of high range resolution profiles and sparse coefficients are used to describe their features. Numerous experiments with the target type number ranging from 2 to 6 have been implemented. Results show that the proposed scheme not only provides higher recognition preciseness in real time, but also achieves more robust performance as the target type number increases.

[1]  Erik Blasch,et al.  Performance model for joint tracking and ATR with HRR radar , 2008, SPIE Defense + Commercial Sensing.

[2]  Bo Chen,et al.  An Efficient Kernel Optimization Method for High Range Resolution Profile Recognition , 2006, 2006 CIE International Conference on Radar.

[3]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Y.D. Shirman,et al.  Computer simulation of aerial target radar scattering recognition, detection, and tracking , 2003, IEEE Aerospace and Electronic Systems Magazine.

[5]  Hedley C. Morris,et al.  Wavelet feature extraction of HRR radar profiles using generalized Gaussian distributions for automatic target recognition , 2005, SPIE Defense + Commercial Sensing.

[6]  Zheng Bao,et al.  Radar high range resolution profiles recognition based on wavelet packet and subband fusion , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[7]  J.O. Miller,et al.  HRR Signature Classification using Syntactic Pattern Recognition , 2008, 2008 IEEE Aerospace Conference.

[8]  John H. Kay,et al.  Classification and Tracking of Moving Ground Vehicles , 2002 .

[9]  Qionghai Dai,et al.  Ways to sparse representation: An overview , 2009, Science in China Series F: Information Sciences.

[10]  Xiyi Hang,et al.  Cancer classification by sparse representation using microarray gene expression data , 2008, 2008 IEEE International Conference on Bioinformatics and Biomeidcine Workshops.

[11]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[12]  M. Skolnik,et al.  Introduction to Radar Systems , 2021, Advances in Adaptive Radar Detection and Range Estimation.

[13]  Feng Zhao,et al.  A New Time-Shift Invariant Feature of Radar HRRPs: A New Time-Shift Invariant Feature of Radar HRRPs , 2011 .

[14]  Zihan Zhou,et al.  Demo: Robust face recognition via sparse representation , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[15]  Anastasios Tefas,et al.  Sparse human movement representation and recognition , 2008, 2008 IEEE 10th Workshop on Multimedia Signal Processing.

[16]  Edoardo Amaldi,et al.  On the Approximability of Minimizing Nonzero Variables or Unsatisfied Relations in Linear Systems , 1998, Theor. Comput. Sci..

[17]  Robert L. Williams,et al.  Synthetic moving target HRR profile generation using measured and modeled target data , 2000, SPIE Defense + Commercial Sensing.

[18]  Erik Blasch.  Modeling intent for a target tracking and identification scenario , 2004, SPIE Defense + Commercial Sensing.

[19]  Michael Lee Bryant,et al.  Standard SAR ATR evaluation experiments using the MSTAR public release data set , 1998, Defense, Security, and Sensing.

[20]  D. Donoho.  For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .

[21]  M. R. Osborne,et al.  A new approach to variable selection in least squares problems , 2000 .

[22]  Yaakov Tsaig,et al.  Fast Solution of $\ell _{1}$ -Norm Minimization Problems When the Solution May Be Sparse , 2008, IEEE Transactions on Information Theory.

[23]  D. Donoho,et al.  Atomic Decomposition by Basis Pursuit , 2001 .

[24]  Zheng Bao,et al.  Radar High Range Resolution Profiles Feature Extraction Based on Kernel PCA and Kernel ICA , 2005, ISNN.

[25]  Bart Kahler,et al.  Preliminary comparison of high-range resolution signatures of moving and stationary ground vehicles , 2002, SPIE Defense + Commercial Sensing.

[26]  Hsueh-Jyh Li,et al.  Using range profiles as feature vectors to identify aerospace objects , 1993 .

[27]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..