基于压缩感知稀疏分解的一维距离像目标识别 -- 西北工业大学学报,2016,34(2):256-261
论文:2016,Vol:34,Issue(2):256-261
引用本文:
段沛沛, 李辉, 李彬. 基于压缩感知稀疏分解的一维距离像目标识别[J]. 西北工业大学学报
Duan Peipei, Li Hui, Li Bin. Radar High Resolution Range Profile Target Recognition Based on Sparse Decomposition in Compressed Sensing[J]. Northwestern polytechnical university

基于压缩感知稀疏分解的一维距离像目标识别
段沛沛1,2, 李辉1, 李彬1
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
摘要:
近年来对压缩感知理论的研究,进一步证明了信号的稀疏表示方法在信号压缩、特征提取等方面的有效性及巨大的应用潜力。作为信号处理领域的典型应用之一,雷达目标识别已有许多成熟的算法,其中一些基于高分辨距离像进行识别,但是这些方法大多忽略了高分辨距离像信号自身的稀疏特点。为此提出了一种基于压缩感知稀疏分解实现高分辨一维距离像目标识别的算法。此算法首先构建组合正交冗余字典,在满足信号表示准确性的情况下,兼有正交字典运算快捷的特点;然后,通过改进的分组匹配稀疏分解算法,根据距离像训练样本快捷地求取其类别字典;最后,基于类别字典对测试样本进行分类实现目标识别。仿真实验证明该目标识别算法简捷、识别率较高、抗噪能力强。
关键词:    压缩感知    雷达目标识别    高分辨距离像    组合正交冗余字典    稀疏分解    信号压缩   
Radar High Resolution Range Profile Target Recognition Based on Sparse Decomposition in Compressed Sensing
Duan Peipei1,2, Li Hui1, Li Bin1
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
Abstract:
In recent years, with the development of compressed sensing theory, sparse representation is widely used in signal compression and feature extraction. This method presents tremendous application potential. Radar target recognition is one of the classic applications of signal processing and there are many recognition algorithms. Some recognition algorithms are based on high resolution range profile(HRRP), but less of them employ the sparseness of HRRP samples. Thus, a radar HRRP target recognition algorithm based on sparse decomposition in compressed sensing is presented here. First, several orthogonal bases are used to compose a redundant dictionary which can satisfy the accuracy and speediness of HRRP sparse representation. Then, the training samples' taxonomic dictionaries are acquired by an improve grouping MP decomposition algorithm. Finally, the reconstruction errors of testing samples were calculated to recognize the targets. The simulation results show that this algorithm has higher recognition rate and better denoising performance. It is easy and practical for radar target recognition.
Key words:    compressed sensing    RTR(radar target recognition)    HRRP(high resolution range profile)    redundant dictionary compiled with orthogonal bases    sparse decomposition    signal compression   
收稿日期: 2015-10-13     修回日期:
DOI:
基金项目: 国家自然科学基金(61571364)资助
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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。
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参考文献:
[1] Keller J B. Geometrical Theroy of Diffraction[J]. Journal of the Optical Society of Americal, 1962,52(2):116-130
[2] 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:Xidian University, 2010(in Chinese)
[3] Candès E J, Romberg J, Tao T. Robust Uncertainty Principles:Exact Signal Reconstruction from Highly Incomplete Frequency Information[J]. IEEE Trans on Information Theory, 2006,52(2):489-509
[4] Goyal V K, Alyson K, et al. Compressive Sampling and Lossy Compression. IEEE Signal Processing Magazine, 2008, 25(2):48-56
[5] 周剑雄,石广志,胡磊,等. 基于频域稀疏非均匀采样的雷达目标一维高分辨成像[J]. 电子学报,2012,40(5):926-934 Zhou Jianxiong, Shi Guangzhi, Hu Lei, et al. Radar Target One Dimensional High Resolution Imaging Based on Sparse and Non-Uniform Samplings in Frequency Domain[J]. Chinese Journal of Electronics, 2012, 40(5):926-934(in Chinese)
[6] 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)
[7] Daniele B, Mark D P. Learning Dictionaries for Sparse Approximation Using Iterative Projections and Rotations[J]. IEEE Trans on Signal Processing, 2013, 61(8):2055-2065
[8] Akcakaya M, Tarokh V. Performance of Sparse Representation Algorithms Using Randomly Generated Frames[J]. IEEE Trans on Signal Processing Letters, 2007,14(11):777-780
[9] 刘丹华,石光明,周佳社. 一种冗余字典下的信号稀疏分解新方法[J]. 西安电子科技大学学报(自然科学版),2008,35(2):228-232 Liu Danhua, Shi Guangming, Zhou Jiashe. New Method for Signal Sparse Decomposition over a Redundant Dictionary[J]. Journal of Xidian University, 2008, 35(2):228-232(in Chinese)
[10] Yang Jianchao, Wang Zhaowen, Lin Zhe, et al. Coupled Dictionary Training for Image Super-Resolution. IEEE Trans on Image Processing, 2012,21(8):3467-3478
[11] 王磊,周乐囡,姬红兵,林琳. 一种面向信号分类的匹配追踪新方法[J]. 电子与信息学报,2014,36(6):1299-1306 Wang Lei, Zhou Lenan, Ji Hongbing, Lin Lin. A New Matching Pursuit Algorithm for Signal Classification[J]. Journal of Electronics and Information Technology, 2014, 36(6):1299-1306(in Chinese)
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