基于结构划分字典学习的雷达目标识别 -- 西北工业大学学报,2015,33(4):672-676
论文:2015,Vol:33,Issue(4):672-676
引用本文:
段沛沛, 李辉, 李琦. 基于结构划分字典学习的雷达目标识别[J]. 西北工业大学学报
Duan Peipei, Li Hui, Li Qi. Radar Target Recognition Based on Structural Dictionary Learning[J]. Northwestern polytechnical university

基于结构划分字典学习的雷达目标识别
段沛沛1,2, 李辉1, 李琦3
1. 西北工业大学 电子信息学院, 陕西 西安 710029;
2. 西安石油大学 计算机学院, 陕西 西安 710065;
3. 西安电子科技大学 电子工程学院, 陕西 西安 710071
摘要:
在使用高分辨距离像进行雷达目标识别时,有时必须面对大样本问题,可实际上雷达在某一时刻观测到的物理过程是很少的,传统的方法在识别过程中从未考虑过距离像信号的稀疏性。为此,文中提出了一种基于结构划分冗余字典完成雷达一维距离像稀疏表示,进而实现目标识别的算法。该算法首先依据字典原子的结构特点划分冗余字典,简化字典表述的同时减少原子数据存储量;随后,采用改进的遗传匹配追踪算法(IGAMP)对一维距离像训练样本进行稀疏分解以获得各类目标的类别字典;最后,根据类别字典分析测试样本的重构误差实现目标识别。仿真实验证明,文中算法简捷、识别率高,即便受到噪声干扰依然能稳健地识别目标。
关键词:    计算机仿真    MATLAB    分类算法    字典学习    改进的遗传匹配追踪算法    雷达目标识别    高分辨距离像    稀疏表示    冗余字典   
Radar Target Recognition Based on Structural Dictionary Learning
Duan Peipei1,2, Li Hui1, Li Qi3
1. Department of Electronics Engineering, Northwestern Polytechnical University, Xi'an 710029, China;
2. School of Computer Science, Xi'an Shiyou University, Xi'an 710065, China;
3. School of Electronic Engineering, Xidian University, Xi'an 710071, China
Abstract:
When high resolution range profile(HRRP) are used to recognize radar target, we need to deal with large sample size problem sometimes. In fact, the physical processes observed by a radar is very limited. None of the traditional methods makes use of the sparseness of HRRP samples. Thus, an redundant dictionary and a fast sparse representation algorithm are used to implement radar target recognition here. First, a Gabor redundant dictionary was partitioned by the characteristics of the atoms in it. By doing this, the atoms storage was decreased and the dictionary was generated faster. Then, the sparse representation algorithm (IGAMP) was used to produce the training samples' taxonomic dictionaries quickly. Finally, the reconstruction errors of testing samples were calculated to recognize the targets. The simulations show that this algorithm has the advantages of conciseness, higher recognition rate and good robustness.
Key words:    computer simulation    MATLAB    taxonomic algorithm    dictionary learning    IGAMP(improved genetic algorithm matching pursuit)    radar target recognition    high resolution range profile    sparse representation    redundant dictionary   
收稿日期: 2015-04-07     修回日期:
DOI:
基金项目: 国家自然科学基金(61171155)与陕西省自然科学基金(2012JM8010)资助
通讯作者:     Email:
作者简介: 段沛沛(1980—),女,西北工业大学博士研究生,主要从事模式识别、雷达数据处理研究。
相关功能
PDF(1013KB) Free
打印本文
把本文推荐给朋友
作者相关文章
段沛沛  在本刊中的所有文章
李辉  在本刊中的所有文章
李琦  在本刊中的所有文章

参考文献:
[1] 柴晶. 雷达高分辨距离像目标识别的拒判算法和特征提取技术研究[D]. 西安:西安电子科技大学,2010 Chai Jing. Study of Rejection Algorithm and Feature Extraction Technique on Radar HRRP Target Recognition[D]. Xi'an: Xidia University,2010 (in Chinese)
[2] Feng Bo, Du Lan, Liu Hongwei, et al.Radar HRRP Target Recognition Based on K-SVD Algorithm[C]//Proc of 2011 IEEE CIE International Conference on Radar Chengdu: IEEE Press, 2011: 642-645
[3] 邹建成,车冬娟. 信号稀疏表示方法研究进展综述[J]. 北方工业大学学报,2013,25(1):1-4 Zou Jiancheng, Che Dongjuan. Research on Signal Sparse Representation[J]. Journal of North China University of Technology, 2013, 25(1):1-4 (in Chinese)
[4] Kenneth Kreutzdelgado, Joseph F Murray, Bhaskar D Rao, et al. Dictionary Learning Algorithms for Sparse Representation[J]. Neural Compute, 2003, 15(2): 349-396
[5] Zeng J Y, Gan J Y, Zhai Y K. Application of Gabor dictionary and SL0 Norm in Disguise Face Recognition[J]. Applied Mechanics and Materials, 2013, 278/279/280:1201-1204
[6] Yang M, Zhang L, Simon C K, et al. Gabor Feature Based Robust Representation and Classification for Face Recognition with Gabor Occlusion Dictionary[J]. Pattern Recognition, 2013, 46(7): 1865-1878
[7] 尹忠科,王建英,邵君. 基于原子库结构特性的信号稀疏分解[J]. 西南交通大学学报,2005,40(2):173-178 Yin Zhongke, Wang Jianying, Shao Jun. Sparse Decomposition Based on Structural Properties of Atom Dictionary[J]. Journal of Southwest Jiaotong University, 2005, 40(2): 173-178 (in Chinese)
[8] Gao Qiang, Duan Chendong, Fang Xiangbo, Liu Benchao. A Study on Matching Pursuit Based on Genetic Algorithms[C]//Proc IEEE Measuring Technology and Mechatronics Automation, New York: IEEE, 2011: 283-286
[9] 张政. 一种基于互相关运算信号稀疏分解的快速算法[J]. 软件导刊,2011,10(7):45-47 Zhang Zheng. A Fast Computation Method for Sparse Decomposition Based on the Cross-Correlation Calculation[J]. Software Guide, 2011,10(7):45-47 (in Chinese)
相关文献:
1.刘亚擎, 廉保旺, 唐成凯.长基线高精度GNSS相对定位算法[J]. 西北工业大学学报, 2013,31(3): 451-456
2.王珺, 彭进业, 何贵青, 冯晓毅.基于多尺度字典学习的图像融合方法[J]. 西北工业大学学报, 2013,31(5): 793-797