Vol. 37

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2014-06-19

Complex HRRP Target Recognition Based on Phase and Amplitude Fusion Analysis

By Jian-Sheng Fu, Hui Zu, Zhi Qiao, and Shao-Fei Wang
Progress In Electromagnetics Research M, Vol. 37, 63-72, 2014
doi:10.2528/PIERM14042204

Abstract

Due to the traditional recognition researches prevalently fastening on HRRP's amplitudes while almost completely neglecting the phases, this paper attempts to directly prove the discriminant availability of HRRP's phases via two proposed fusion recognition strategies. The first strategy includes three sub-processes, respectively, based on phase cosine, phase sine and their fusion. The second strategy also includes three sub-processes, respectively, based on phases, amplitudes and their fusion. Additionally, a trigonometric function couple (TFC) method is used to reduce the phase sensitivity. Several measured experimental results indicate as follows. Firstly, employing TFC can perform much better. Secondly, the two fusion recognition sub-processes apparently outperform the corresponding subprocesses constructing them. Finally, phase information usually has a better noise immunity compared with amplitude information, and fusing phase information into amplitudes may improve the traditional recognition performance. Therefore, the availabilities of HRRP's phases and the two fusion strategies have been experimentally proven.

Citation


Jian-Sheng Fu, Hui Zu, Zhi Qiao, and Shao-Fei Wang, "Complex HRRP Target Recognition Based on Phase and Amplitude Fusion Analysis," Progress In Electromagnetics Research M, Vol. 37, 63-72, 2014.
doi:10.2528/PIERM14042204
http://www.jpier.org/PIERM/pier.php?paper=14042204

References


    1. Xing, M. and B. Bao, "The properties of range profile of aircraft," Chin. J. Electron., Vol. 11, No. 1, 1-6, 2002.

    2. Du, L., et al., "Bayesian spatiotemporal multitask learning for radar HRRP target recognition," IEEE Trans. Signal Process., Vol. 59, No. 7, 3182-3196, 2011.
    doi:10.1109/TSP.2011.2141664

    3. Han, S.-K., H.-T. Kim, S.-H. Park, and K.-T. Kim, "Efficient radar target recognition using a combination of range profile and time-frequency analysis," Progress In Electromagnetics Research, Vol. 108, 131-140, 2010.
    doi:10.2528/PIER10071601

    4. Shi, L., et al., "Radar HRRP statistical recognition with local factor analysis by automatic Bayesian Ying-Yang harmony learning," IEEE Trans. Signal Process., Vol. 59, No. 2, 610-617, 2011.
    doi:10.1109/TSP.2010.2088391

    5. Du, L., et al., "A two-distribution compounded statistical model for radar HRRP target recognition," IEEE Trans. Signal Process., Vol. 54, No. 61, 2226-2238, 2006.

    6. Du, L., et al., "Radar automatic target recognition using complex high-resolution range profiles," IET Radar Sonar Navig., Vol. 1, No. 1, 18-26, 2007.
    doi:10.1049/iet-rsn:20050119

    7. Liao, K. and W. Yang, "Extraction of radar target length based on high resolution range profile," Proc. --- Int. Conf. Electr. Control Eng., ICECE, 956-959, Jun. 26-28, 2010.

    8. Astola, J. T., et al., "Reduction of aspect dependent speckle °uctuations in high-resolution radar range profiles," Telecommun. Radio Eng., Vol. 69, No. 8, 687-698, 2010.
    doi:10.1615/TelecomRadEng.v69.i8.40

    9. Zhang, R., et al., "Analysis about the speckle of radar high resolution range profile," Sci. China Technol. Sci., Vol. 54, No. 1, 226-236, 2011.
    doi:10.1007/s11431-010-4207-x

    10. Fu, J.-S. and W.-L. Yang, "KFD-based multiclass synthetical discriminant analysis for radar HRRP recognition," Journal of Electromagnetic Waves and Applications, Vol. 26, No. 2-3, 169-178, 2012.
    doi:10.1163/156939312800030947

    11. Zhou, D., X. Shen, and W. Yang, "Radar target recognition based on fuzzy optimal transformation using high-resolution range profile," Pattern Recogn. Lett., Vol. 34, No. 3, 256-264, 2013.
    doi:10.1016/j.patrec.2012.10.010

    12. Cheng, B., et al., "Large margin feature weighting method via linear programming," IEEE Trans. Knowl. Data. Eng., Vol. 21, No. 10, 1475-1488, 2009.
    doi:10.1109/TKDE.2008.238

    13. Zhou, D., et al., "Orthogonal kernel projecting plane for radar HRRP recognition," Neurocomputing, Vol. 106, 61-67, 2013.
    doi:10.1016/j.neucom.2012.10.016

    14. Fu, J.-S., K. liao, and W.-L. Yang, "Radar HRRP target recognition using multi-KFD-based LDA algorithm," Progress In Electromagnetics Research C, Vol. 34, 15-26, 2012.
    doi:10.2528/PIERC11121804

    15. Lee, S.-J., I.-S. Choil, B. Cho, E. J. Rothwell, and A. K. Temme, "Performance enhancement of target recognition using feature vector fusion of monostatic and bistatic radar," Progress In Electromagnetics Research, Vol. 144, No. 10, 291-302, 2014.
    doi:10.2528/PIER13103101

    16. Zhang, L. and W.-D. Zhou, "Sparse ensembles using weighted combination methods based on linear programming," Pattern Recogn., Vol. 44, No. 1, 97-106, 2011.
    doi:10.1016/j.patcog.2010.07.021

    17. Fu, J., X. Deng, and W. Yang, "Radar HRRP recognition based on discriminant information analysis," WSEAS Trans. Inf. Sci. Appl., Vol. 8, No. 4, 185-201, 2011.

    18. Du, L., et al., "Noise robust radar HRRP target recognition based on multitask factor analysis with small training data size," IEEE Trans. Signal Process., Vol. 60, No. 7, 3546-3559, 2012.
    doi:10.1109/TSP.2012.2191965

    19. Swets, D. and J. Weng, "Using discriminant eigenfeatures for image retrieval," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 8, No. 2, 831-836, 1996.
    doi:10.1109/34.531802

    20. Gou, J., L. Du, and T. Xiong, "Weighted k-nearest centroid neighbor classification," J. Comput. Inf. Syst., Vol. 8, No. 2, 851-860, 2012.