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基于t分布扩展概率主成分分析模型的一维距离像识别方法

李彬 李辉 郭淞云

李彬, 李辉, 郭淞云. 基于t分布扩展概率主成分分析模型的一维距离像识别方法[J]. 电子与信息学报, 2017, 39(8): 1857-1864. doi: 10.11999/JEIT161220
引用本文: 李彬, 李辉, 郭淞云. 基于t分布扩展概率主成分分析模型的一维距离像识别方法[J]. 电子与信息学报, 2017, 39(8): 1857-1864. doi: 10.11999/JEIT161220
LI Bin, LI Hui, GUO Songyun. Using t-distribution Based Probabilistic Principal Component Analysis Model for High Resolution Range Profile Recognition[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1857-1864. doi: 10.11999/JEIT161220
Citation: LI Bin, LI Hui, GUO Songyun. Using t-distribution Based Probabilistic Principal Component Analysis Model for High Resolution Range Profile Recognition[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1857-1864. doi: 10.11999/JEIT161220

基于t分布扩展概率主成分分析模型的一维距离像识别方法

doi: 10.11999/JEIT161220
基金项目: 

国家自然科学基金(61571364),西北工业大学研究生创意创新种子基金(Z2017022)

Using t-distribution Based Probabilistic Principal Component Analysis Model for High Resolution Range Profile Recognition

Funds: 

The National Natural Science Foundation of China (61571364), The Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Poly-technical University (Z2017022)

  • 摘要: 该文针对概率主成分分析(PPCA)模型用于1维高分辨距离像(HRRP)识别对噪声敏感的问题,对经典PPCA模型进行修正。该方法将基于高斯分布的PPCA模型扩展为基于t分布的PPCA模型,能够综合利用t分布对噪声稳健和PPCA模型自由参数少的特性。同时为了减少目标方位敏感性对HRRP统计建模的影响,进一步将t分布模型扩展为混合概率t分布模型,能够以分布趋同的原则将不同方位帧内具有相同统计特性的HRRP数据进行聚类,减少模型的失配,改善识别性能。模型参数通过期望最大值(EM)算法估计,可提高计算效率。最后,通过贝叶斯规则,以获取的统计特征识别测试数据,仿真结果表明该方法能够提高低信噪比条件下PPCA模型的稳健性。
  • LIU Hongwei, CHEN Bo, FENG B, et al. Radar high- resolution range profiles target recognition based on stable dictionary learning[J]. IET Radar, Sonar Navigation, 2016, 10(2): 228-237. doi: 10.1049/iet-rsn.2015.0007.
    ZHOU Daiying. Radar target HRRP recognition based on reconstructive and discriminative dictionary learning[J]. Signal Processing, 2016, 126: 52-64. doi: 10.1016/j.sigpro. 2015.12.006.
    PAN Xiaoyi, WANG Wei, FENG D, et al. Signature extraction from rotating targets based on a fraction of HRRPs[J]. IEEE Transactions on Antennas and Propagation, 2015, 63(2): 585-592. doi: 10.1109/TAP.2014.2379955.
    PAN Mian, JIANG Jie, LI Zhu, et al. Radar HRRP recognition based on discriminant deep auto-encoders with small training data size[J]. Electronics Letters, 2016, 52(20): 1725-1727. doi: 10.1049/el.2016.3060.
    DU L, HE H, ZHAO L, et al. Noise robust radar HRRP target recognition based on scatter matching algorithm[J]. IEEE Sensors Journal, 2016, 16(6): 1743-1753. doi: 10.1109/JSEN. 2015.2501850.
    LUNDAN J and KOIVUNEN V. Deep learning for HRRP- based target recognition in multi-static radar systems[C]. IEEE Radar Conference, Philadelphia, PA, 2016. doi: 10.1109 /RADAR.2016.7485271.
    JACOB S P and OSULLIVAN J A. Automatic target recognition using sequences of high resolution radar range- profiles[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(2): 364-381. doi: 10.1109/7.845214.
    WEBB A R. Gamma mixture models for target recognition[J]. Pattern Recognition, 2000, 33(12): 2045-2054. doi: 10.1016/ S0031-3203(99)00195-8.
    COPSEY K D and WEBB A R. Bayesian gamma mixture model approach to radar target recognition[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1201-1217. doi: 10.1109/TAES.2003.1261122.
    DU L, LIU H, BAO Z, et al. A two-distribution compounded statistical model for Radar HRRP target recognition[J]. IEEE Transactions on Signal Processing, 2006, 54(6): 2226-2238. doi: 10.1109/TSP.2006.873534.
    WANG Caiyun and XIE Jiling. Radar high resolution range profile target recognition based on t-mixture model[C]. IEEE Radar Conference, Kansas, 2011: 762-767. doi: 10.1109/ RADAR.2011.5960640.
    DU L, LIU H, BAO Z, et al. Radar HRRP statistical recognition: Parametric model and model selection[J]. IEEE Transactions on Signal Processing, 2008, 56(5): 1931-1944. doi: 10.1109/TSP.2007.912283.
    LIU Hongwei, DU Lan, WANG Penghui, et al. Radar HRRP automatic target recognition: Algorithms and applications[C]. Proceedings of IEEE CIE International Conference on Radar, Chengdu, 2011: 14-17. doi: 10.1109/CIE-Radar.2011. 6159709.
    王鹏辉, 杜兰, 刘宏伟, 等. 雷达高分辨距离像分帧新方法[J].西安电子科技大学学报, 2011, 38(6): 22-29. doi: 10.3969 /j.issn.1001-2400.2011.06.004.
    WANG P H, DU L, LIU H W, et al. New frame segmentation method for radar HRRPs[J]. Journal of Xidian University, 2011, 38(6): 22-29. doi: 10.3969/j.issn.1001-2400.2011.06. 004.
    王鹏辉, 杜兰, 刘宏伟. 基于复高斯模型的雷达高分辨距离像目标识别新方法[J]. 光学学报, 2014, 34(2): 1-10. doi: 10.3788 /AOS201434.0228004.
    WANG P H, DU L, and LIU H W. A new method based on complex Gaussian models for radar high resolution range profile target recognition[J]. Acta Optica Sinica, 2014, 34(2): 1-10. doi: 10.3788/AOS201434.0228004.
    CHEN T, MARTIN E B, MONTAGUE G A, et al. Robust probabilistic PCA with missing data and contribution analysis for outlier detection[J]. Computational Statistics Data Analysis, 2009, 53(10): 3706-3716. doi: 10.1016/j.csda. 2009.03.014.
    LANGE K L, LITTLE R J, TAYLOR J M, et al. Robust statistical modeling using the t distribution[J]. Journal of the American Statistical Association, 2012, 84(408): 881-896. doi: 10.1080/01621459.1989.10478852.
    TIPPING M E and BISHOP C M. Probabilistic principal component analysis[J]. Journal of The Royal Statistical Society Series B Statistical Methodology, 1999, 61(3): 611-622. doi: 10.1111/1467-9868.00196.
    ZHOU X and LIU X. The EM algorithm for the extended finite mixture of the factor analyzers model[J]. Computational Statistics Data Analysis, 2008, 52(8): 3939-3953. doi: 10.1016/j.csda.2008.01.023.
    PEEL D and MCLACHLAN G J. Robust mixture modelling using the t distribution[J]. Statistics and Computing, 2000, 10(4): 339-348. doi: 10.1023/A:1008981510081.
    TIPPING M E and BISHOP C M. Mixtures of probabilistic principal component analyzers[J]. Neural Computation, 1999, 11(2): 443-482. doi: 10.1162/089976699300016728.
    ZHU D, LIU Y, HUO K, et al. A novel high-precision phase- derived-range method for direct sampling LFM radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(2): 1131-1141. doi: 10.1109/TGRS.2015.2474144.
    但波, 姜永华, 李敬军, 等. 雷达高分辨距离像自适应角域划分方法[J]. 系统工程与电子技术, 2014, 36(11): 2178-2185. doi: 10.3969/j.issn.1001-506X.2014.11.11.
    DAN B, JIANG Y H, LI J J, et al. Adaptive angular-sector segmentation method for radar HRRP[J]. Systems Engineering and Electronics, 2014, 36(11): 2178-2185. doi: 10.3969/j.issn.1001-506X.2014.11.11.
    黄得双. 高分辨雷达智能信号处理技术[M]. 北京: 机械工业出版社, 2001: 19-31.
    HUANG D S. Intelligent Signal Processing Technique for High Resolution Radars[M]. Beijing: China Machine Press, 2001: 19-31.
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出版历程
  • 收稿日期:  2016-11-10
  • 修回日期:  2017-04-06
  • 刊出日期:  2017-08-19

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