基于核主分量相关判别分析特征提取方法的目标HRRP识别

李龙; 刘峥

doi:10.11999/JEIT170329

基于核主分量相关判别分析特征提取方法的目标HRRP识别

doi: 10.11999/JEIT170329

李龙¹,
刘峥¹

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- 被引次数: 0
出版历程
- 收稿日期: 2017-04-14
- 修回日期: 2017-07-10
- 刊出日期: 2018-01-19

Kernel Principal Component Correlation and Discrimination Analysis Feature Extraction Method for Target HRRP Recognition

LI Long¹,
LIU Zheng¹

摘要

摘要: 为有效提高雷达高分辨1维距离像目标识别系统的总体性能，需要对目标高分辨1维距离像进行特征提取，以得到具有最小信息损失、高可分性且低维度的目标特征，为实现该目的提出一种基于核主分量相关判别分析的特征提取算法。该算法基于目标高分辨1维距离像的统计特性，通过对核主分量分析中核函数的选择，实现对不同类型距离单元的特征提取。同时综合线性判别分析与典型相关分析理论构建新的准则函数，以实现特征空间中类内相关性与类间差异性最大化，同时减少目标特征中的冗余信息。利用实测数据进行实验，结果表明该方法提高了特征向量的可分性，降低了特征向量的维度，并且对该算法在不同强度杂波下的识别性能进行了分析，实验结果表明，该方法可以有效的提高目标高分辨1维距离像目标识别系统的总体性能。
- 高分辨距离像目标识别 /
- 特征提取 /
- 核主分量分析 /
- 线性判别分析 /
- 典型相关分析
Abstract: For radar High Resolution Range Profile (HRRP) automatic target recognition, the features should be extracted with sufficient target information, high discrimination, noise robustness, and low feature vector dimension. However, radar HRRP recognition suffers from insufficient amount of information and low discrimination feature, besides the radar recognition system also need the ability of real-time processing with low dimension. To obtain features with merits of low-dimension and high-discrimination, a novel feature extraction method is designed for radar high range resolution profile, namely Kernel Principal Component Correlation and Discrimination Analysis (KPCCDA). With the proposed method, the statistical characteristics of different scatter range cells can be effectively used by Kernel Principal Component Analysis (KPCA). And the within-class correlation and between- class discrimination are maximized with linear discrimination analysis and canonical correlation analysis used. Besides, the redundancy and dimensionality of the feature vectors are reduced, yielding a lowered computational complexity to meet the storage requirement in practical radar target recognition. Experimental results with measured data validate the efficiency of the proposed method.

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参考文献(20)

WU Jiani, CHEN Yongguang, DAI Dahai, et al. Target recognition for polarimetric HRRP based on fast density search clustering method[J]. Journal of Electronics Information Technology, 2016, 38(10): 2461-2467. doi: 10.11999/JEIT151457.

吴佳妮, 陈永光, 代大海, 等. 基于快速密度搜索聚类算法的极化HRRP分类方法[J]. 电子与信息学报, 2016, 38(10): 2461-2467. doi: 10.11999/JEIT151457.

徐彬, 陈渤, 刘宏伟, 等. 基于注意循环神经网络模型的雷达高分辨率距离像目标识别[J]. 电子与信息学报, 2016, 38(12): 2988-2995. doi: 10.11999/JEIT161034.

XU Bin, CHEN Bo, LIU Hongwei, et al. Attention-based recurrent neural network model for radar high-resolution range profile target recognition[J]. Journal of Electronics Information Technology, 2016, 38(12): 2988-2995. doi: 10. 11999/JEIT161034.

李龙, 刘峥. 基于训练特征空间分布的雷达地面目标鉴别器设计[J]. 电子与信息学报, 2016, 38(4): 950-957. doi: 10.11999 /JEIT150786.

LI Long and LIU Zheng. Identifier for radar ground target based on distribution of space of training features[J]. Journal of Electronics Information Technology, 2016, 38(4): 950-957. doi: 10.11999/JEIT150786.

DU Lan, HE Hua, ZHAO Le, et al. Noise robust radar HRRP target recognition based on scatterer matching algorithm[J]. IEEE Sensors Journal, 2016, 16(6): 1743-1753. doi: 10.1109/ JSEN.2015.2501850.

但波, 姜永华, 李敬军, 等. 基于空时融合隐马尔科夫模型的舰艇编队目标识别方法[J]. 电子与信息学报, 2015, 37(4): 926-932. doi: 10.11999/JEIT140589.

DAN Bo, JIANG Yonghua, LI Jingjun, et al. Ship formation target recognition based on spatial and temporal fusion hidden Markov model[J]. Journal of Electronics Information Technology, 2015, 37(4): 926-932. doi: 10.11999/ JEIT140589.

WANG Jianqiao, LI Yuehua, and CHEN Kun. Radar high- resolution range profile recognition via geodesic weighted sparse representation[J]. IET Radar, Sonar Navigation, 2015, 9(1): 75-83. doi: 10.1049/iet-rsn.2014.0113.

DU Lan, LIU Hongwei, BAO Zheng, et al. Radar HRRP target recognition based on higher order spectra[J]. IEEE Transactions on Signal Processing, 2005, 53(7): 2359-2368. doi: 10.1109/TSP.2005.849161.

WONG Weijing, TEOH Andrew, KHO Yauhee, et al. Kernel PCA enabled bit-string representation for minutiae-based cancellable fingerprint template[J]. Pattern Recognition, 2016, 51: 197-208. doi: 10.1016/j.patcog.2015.09.032.

WANG Qingwang, GU Yanfeng, and TUIA Devis. Discriminative multiple kernel learning for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(7): 3912-3927. doi: 10.1109/TGRS. 2016.2530807.

LIN Da, XU Xin, and PU Fangling. Multiple feature fusion using a multiset aggregated canonical correlation analysis for high spatial resolution satellite image scene classification[C]. IEEE Geoscience and Remote Sensing Symposium, Milan, Italy, 2015: 481-484.

DU Lan, LIU Hongwei, BAO Zheng, 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.

DIAZ-MORALES Roberto and NAVIA-VAZQUEZ Angel. Efficient parallel implementation of kernel methods[J]. Neurocomputing, 2016, 191: 175-186. doi: 10.1016/j.neucom. 2015.11.097.

ZHANG Ziming and TORR Philip. Object proposal generation using two-stage cascade SVMs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(1): 102-115. doi: 10.1109/TPAMI.2015.2430348.

SHUI Penglang, XU Shuwen, and LIU Hongwei. Range-spread target detection using consecutive HRRPs[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(1): 647-665. doi: 10.1109/TAES.2011.5705697.

DU Wanwen, WANG Fang, SHENG Weixing, et al. Modeling and simulation of radar echo signal of aircraft targets with GRECO[C] International Symposium on Antennas, Propagation and EM Theory. Kunming, China, 2009: 859-862.

KONG Yu and FU Yun. Max-margin action prediction machine[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(9): 1844-1858. doi: 10.1109/ TPAMI.2015.2491928.

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