Abstract
This paper studies the problem of radar target recognition based on radar cross section (RCS) observation sequence. First, the authors compute the discrete wavelet transform of RCS observation sequence and extract a valid statistical feature vector containing five components. These five components represent five different features of the radar target. Second, the authors establish a set-valued model to represent the relation between the feature vector and the authenticity of the radar target. By set-valued identification method, the authors can estimate the system parameter, based on which the recognition criteria is given. In order to illustrate the efficiency of the proposed recognition method, extensive simulations are given finally assuming that the true target is a cone frustum and the RCS of the false target is normally distributed. The results show that the set-valued identification method has a higher recognition rate than the traditional fuzzy classification method and evidential reasoning method.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Huang P K, Yin H C, and Xu X J, Radar Target Characteristics, Publishing House of Electronics Industry, Beijing, 2004.
Knott E F, Shaeffer J F, and Tuley M T, Radar Cross Section, 2nd Edition, SciTech Publishing Inc, Raleigh, 2004.
Ruck G T, Radar Cross Section Handbook, Plenum Press, New York, 1970.
Huang J, The study on feature extraction from RCS of the space target, Master Thesis of National University of Defense Technology, Changsha, Hunan, 2009.
Franques V T and Kerr D A, Wavelet-based rotationally invariant target classfication, SPIE, 1997, 3068: 102–112.
Duda R O, Hart P E, and Stork D G, Pattern Classification, 2nd Edition, John Wiley Sons Inc, New York, 2001.
Carlin B P and Louis T A, Bayes and Empirical Bayes Methods for Data Analysis, 2nd Edition, Chapman Hall, Boca Raton, 2000.
Carlin B P and Louis T A, Bayes and empirical Bayes methods for data analysis, Statistics and Computing, 1997, 7(2): 153–154.
Srivastava R P, An introduction to evidential reasoning for decision making under uncertainty: Bayesian and belief functions perspectives, International Journal of Accounting Information Systems, 2010, 12: 126–135.
Yang J B, An evidential reasoning approach for multiple-attribute decision making with uncertainty, IEEE Transaction on System, Man, and Cybernetics, 1994, 24: 1–18.
Miao C D and Gao G M, The application of Dempster-Shafer evidence theory in radar target recognition, Radar and Confrontation, 2008, 3: 32–34.
Ma J G, Feature extraction and recognition of the space target, PhD Thesis of National University of Defense Technology, Changsha, Hunan, 2006.
Fu Y W, Radar target fusion recognition, PhD Thesis of National University of Defense Technology, Changsha, Hunan, 2003.
Jozef Tkac, Stefan Spirko, and Ladislav Boka, Radar object recognition by wavelet transform and neural network, 13th International Conference on Microwave, Radar and Wireless Communication, 2000, 1: 239–243.
Wang N, Chen W G, and Zhang X G, Automatic target recognition of ISAR object images based on neural network, Proceeding of the International Conference on Neural Networks and Signal Processing, 2003, 1: 373–376.
Zhao Y L, Zhang J F, and Guo J, System identification and adaptive control of set-valued systems, Journal of Systems Science and Mathematical Science, 2012, 32(10): 1257–1265 (in Chinese).
Guo J, Zhang J F, and Zhao Y L, Adaptive tracking of a class of first-order systems with binaryvalued observations and fixed thresholds, Journal of Systems Science and Complexity, 2012, 25(6): 1041–1051.
Bi W J, Zhao Y L, Liu C X, and Yue W H, Set-valued analysis for genome-wide association studies of complex diseases, The 32nd Chinese Control Conference (CCC), 2013, 8262–8267.
Bi W J and Zhao Y L, Iterative parameter estimate with batched binary-valued observations: Convergence with an exponential rate, The 19th World Congress of the International Federation of Automatic Control, 2014, 3220–3225.
Zhou W X, BMD Radar Target Recognition Technology, Publishing House of Electronics Industry, Beijing, 2011.
Mahafza B R, Radar Systems Analysis and Design Using Matlab, CRC Press, Florida, 2000.
Author information
Authors and Affiliations
Corresponding author
Additional information
This research was supported by the National Natural Science Foundation of China under Grant No. 61174042 and the National Key Basic Research Program of China (973 Program) under Grant No. 2014CB845301.
This paper was recommended for publication by Editor-in-Chief GAO Xiao-Shan.
Rights and permissions
About this article
Cite this article
Wang, T., Bi, W., Zhao, Y. et al. Radar target recognition algorithm based on RCS observation sequence — set-valued identification method. J Syst Sci Complex 29, 573–588 (2016). https://doi.org/10.1007/s11424-015-4151-8
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11424-015-4151-8