基于训练特征空间分布的雷达地面目标鉴别器设计

李龙; 刘峥

doi:10.11999/JEIT150786

基于训练特征空间分布的雷达地面目标鉴别器设计

doi: 10.11999/JEIT150786

李龙¹,
刘峥¹

计量
- 文章访问数: 1218
- HTML全文浏览量: 113
- PDF下载量: 380
- 被引次数: 0
出版历程
- 收稿日期: 2015-06-29
- 修回日期: 2015-12-25
- 刊出日期: 2016-04-19

Identifier for Radar Ground Target Based on Distribution of Space of Training Features

LI Long¹,
LIU Zheng¹

摘要

摘要: 该文对雷达地面目标高分辨1维距离像目标识别中的库外目标鉴别问题，提出一种基于训练特征空间分布的雷达地面目标鉴别器。在训练阶段利用基于相关系数预处理的K-Means聚类方法对库内目标样本特征空间进行区域划分，并采用基于空间分布的支撑向量域描述方法确定样本特征空间的边界与支撑向量，利用样本特征空间边界与加权K近邻原则对目标类别进行判决。该方法解决了库内目标与库外目标的鉴别问题，提高了目标识别系统的总体性能。针对多种不同姿态下目标特征空间非均匀聚合的特点，对训练样本特征空间进行区域划分，减小模板匹配搜索运算规模，保证目标鉴别所需的实时性工作要求。最后通过仿真和实测数据验证了该方法具备优良的鉴别性能与良好的实时处理能力。
- 目标鉴别 /
- 高分辨距离像 /
- K-Means聚类 /
- 支撑向量域描述 /
- K近邻分类器
Abstract: To identify the out-of-database targets in the process of radar ground target recognition with High Resolution Range Profile (HRRP), this paper proposes an improved radar ground target identifier based on the distribution of the space of training features. In the training phase, a K-Means clustering strategy based on the pre-process of correlation coefficient is utilized to divide the space of training dataset. Then each sub-space boundary is determined by Support Vector Domain Description (SVDD) based on the distribution of the sample space. Finally, it can decide the category of target with the sub-space boundary and the weighted K-neighbors principle. This method can work without the template of out-of-database samples, which improves the effectiveness of target identification. Due to the fact that the feature space of different targets has the characteristic of non-uniform aggregation under different attitudes, a procedure of region partition is applied to training dataset. Thus computational load is relieved with a decrease in search operation of template matching. The requirement of real-time processing can be satisfied. Finally, the experiments against both simulation and real data verify its excellent performance of identification and real-time processing capability.
- Target identification /
- High Resolution Range Profile (HRRP) /
- K-Means clustering /
- Support Vector Domain Description (SVDD) /
- K Nearest Neighbors (KNN) classifier

HTML全文

参考文献(28)

KHAN N, KSANTINI R, AHMAD I, et al. Covariance- guided one-class support vector machine[J]. Pattern Recognition, 2014, 47(6): 2165-2177.

CABRAL G and OLIVEIRA A. One-class classification based on searching for the problem features limits[J]. Expert Systems with Applications, 2014, 41(11): 7182-7199.

HE X, MOUROT G, MAQUIN D, et al. Multi-task learning with one-class SVM[J]. Neurocomputing, 2014, 133(6): 416-426.

ZHANG H, CAO L, and GAO S. A locality correlation preserving support vector machine[J]. Pattern Recognition, 2014, 47(9): 3168-3178.

DAGEFU F and SARABANDI K. High-resolution subsurface imaging of deeply submerged targets based on distributed near-ground sensors[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 1089-1098.

CHAI Jing, LIU Hongwei, CHEN Bo, et al. Large margin nearest local mean classifier[J]. Signal Processing, 2010, 90(1): 236-248.

TOHME M and LENGELLE R. Maximum margin one class support vector machines for multiclass problems[J]. Pattern Recognition Letters, 2011, 32(10): 1652-1658.

丁军, 刘宏伟, 王英华. 基于非负稀疏表示的SAR图像目标识别方法[J]. 电子与信息学报, 2014, 36(9): 2194-2200. doi: 10.3724/SP.J.1146.2013.01451.

DING Jun, LIU Hongwei, and WANG Yinghua. SAR image target recognition based on non-negative sparse representation[J]. Journal of Electronics Information Technology, 2014, 36(9): 2194-2200. doi: 10.3724/SP.J.1146. 2013.01451.

王军, 赵宜楠, 乔晓林. 基于压缩感知的雷达前视向稀疏目标分辨[J]. 电子与信息学报, 2014, 36(8): 1978-1984. doi: 10. 3724/SP.J.1146.2013.01936.

WANG Jun, ZHAO Yinan, and QIAO Xiaolin. A sparse target-scenario determination strategy based on compressive sensing for active radar in the line of sight[J]. Journal of Electronics Information Technology, 2014, 36(8): 1978-1984. doi: 10.3724/SP.J.1146.2013.01936.

刘艳红, 薛安荣, 史习云. K-means聚类与SVDD结合的新的分类算法[J]. 计算机应用研究, 2010, 27(3): 883-886.

LIU Yanhong, XUE Anrong, and SHI Xiyun. New classification algorithm K-means clustering combined with SVDD[J]. Application Research of Computers, 2010, 27(3): 883-886.

KEMMLER M, RODNER E, WACKER E, et al. One-class classification with gaussian processes[J]. Pattern Recognition, 2013, 46(12): 3507-3518.

BOYD Stephen. Convex Optimization[M]. Cambridge: Cambridge University Press, 2004: 325-337.

柴晶, 刘宏伟, 保铮. 加权KNN分类器在HRRP库外目标拒判中的应用[J]. 系统工程与电子技术, 2010, 32(4): 718-723.

CHAI Jing, LIU Hongwei, and BAO Zheng. Application of a weighted KNN classifier for HRRP out-of-database target rejection[J]. Systems Engineering and Electronics, 2010, 32(4): 718-723.

TOYAMA J, KUDO M, and IMAI H. Probably correct k-nearest neighbor search in high dimensions[J]. Pattern Recognition, 2010, 43(4): 1361-1372.

CHA M, KIM S, and BAEK J. Density weighted support vector data description[J]. Expert Systems with Applications, 2014, 41(6): 3343-3350.

DJOUADI A and BOUKTACHE E. A fast algorithm for the nearest-neighbor classifier[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(3): 277-282.

XIAO Y, WANG H, XU W, et al. L1 norm based KPCA for novelty detection[J]. Pattern Recognition, 2013, 46(1): 389-396.

陈思宝, 陈道然, 罗斌. 基于L1-范数的二维线性判别分析[J]. 电子与信息学报, 2015, 37(6): 1372-1377. doi: 10.11999/ JEIT141093.

CHEN Sibao, CHEN Daoran, and LUO Bin. L1-norm based two-dimensional linear discriminant analysis[J]. Journal of Electronics Information Technology, 2015, 37(6): 1372-1377. doi: 10.11999/JEIT141093.

SAMET H. K-nearest neighbor finding using MaxNearestDist[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(2): 243-252.

刘家辰, 苗启广, 曹莹, 等. 基于混合多样性生成与修剪的集成单类分类算法[J]. 电子与信息学报, 2015, 37(2): 386-393. doi: 10.11999/JEIT140161.

LIU Jiachen, MIAO Qiguang, CAO Ying, et al. Ensemble one-class classifiers based on hybrid diversity generation and pruning[J]. Journal of Electronics Information Technology, 2015, 37(2): 386-393. doi: 10.11999/JEIT140161.

冯博, 陈渤, 王鹏辉, 等. 基于稳健深层网络的雷达高分辨距离像目标特征提取算法[J]. 电子与信息学报, 2014, 36(12): 2949-2955. doi: 10.3724/SP.J.1146.2014.00808.

FENG Bo, CHEN Bo, WANG Penghui, et al. Feature extraction method for radar high resolution range profile targets based on robust deep networks[J]. Journal of Electronics Information Technology, 2014, 36(12): 2949-2955. doi: 10.3724/SP.J.1146.2014.00808.

施引文献

资源附件(0)

访问统计