Volume 6 Issue 3
Jun.  2017
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2017  >  6(3): 239-251
Chen Xiaolong, Guan Jian, He You, Yu Xiaohan. High-resolution Sparse Representation and Its Applications in Radar Moving Target Detection[J]. Journal of Radars, 2017, 6(3): 239-251. doi: 10.12000/JR16110
Citation: Chen Xiaolong, Guan Jian, He You, Yu Xiaohan. High-resolution Sparse Representation and Its Applications in Radar Moving Target Detection[J]. Journal of Radars, 2017, 6(3): 239-251. doi: 10.12000/JR16110
shu

High-resolution Sparse Representation and Its Applications in Radar Moving Target Detection

DOI: 10.12000/JR16110
  • ①.

    Department of Electronic and Information Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, China

  • ②.

    Institute of Information Fusion, Naval Aeronautical and Astronautical University, Yantai 264001, China

Funds:  The National Natural Science Foundation of China (61501487, 61401495, U1633122, 61471382, 61531020), The Natural Science Foundation of Shandong Province (2015ZRA06052), The Aeronautical Science Foundation of China (20162084005, 20162084006, 20150184003), The Young Elite Scientist Program of CAST and Special Funds of Taishan Scholars
  • Received Date: 2016-09-29
  • Rev Recd Date: 2017-02-21
    • Available Online: 2017-03-24
  • Publish Date: 2017-06-28
    Abstract
  • To address difficulties in radar signal processing, the effective and efficient detection of low-observable moving targets in complex environments is an ongoing research hotspot. On the one hand, a signal may be extremely weak due to strong clutter and the complex motion of a target, making it hard to separate them in the time and frequency domains. On the other hand, complex coherent integration methods and the heavy computational burden of long-time integration represent challenges for improving radar detection performance with limited resources. High-resolution sparse representation can separate clutter from a moving target with respect to signal sparsity, and can be regarded as an extension of traditional transform-based moving target detection methods. This method has promising application prospects due to the advantages of its high time-frequency resolution, anti-noise property, robustness, and suitability for the analysis of multi-signals. In this paper, we systematically review conventional radar moving target detection methods. Then, we summarize their applications, including sparse representation in clutter property analysis, suppression, moving target detection, signature extraction, and time-frequency analysis. Next, we consider future developments. Finally, we provide some results based on real datasets and existing research.

     

    • FullText(HTML)
  • loading
    • References(69)
  • [1]
    杨建宇. 雷达技术发展规律和宏观趋势分析[J]. 雷达学报, 2012, 1(1): 19–27. http://radars.ie.ac.cn/CN/abstract/abstract5.shtml

    Yang Jian-yu. Development laws and macro trends analysis of radar technology[J]. Journal of Radars, 2012, 1(1): 19–27. http://radars.ie.ac.cn/CN/abstract/abstract5.shtml
    [2]
    何友, 黄勇, 关键, 等. 海杂波中的雷达目标检测技术综述[J]. 现代雷达, 2014, 36(12): 1–9. doi: 10.3969/j.issn.1004-7859.2014.12.001

    He You, Huang Yong, Guan Jian, et al.. An overview on radar target detection in sea clutter[J]. Modern Radar, 2014, 36(12): 1–9. doi: 10.3969/j.issn.1004-7859.2014.12.001
    [3]
    陈小龙, 关键, 何友. 微多普勒理论在海面目标检测中的应用及展望[J]. 雷达学报, 2013, 2(1): 123–134. http://radars.ie.ac.cn/CN/abstract/abstract68.shtml

    Chen Xiao-long, Guan Jian, and He You. Applications and prospect of micro-motion theory in the detection of sea surface target[J]. Journal of Radars, 2013, 2(1): 123–134. http://radars.ie.ac.cn/CN/abstract/abstract68.shtml
    [4]
    Zuo Lei, Li Ming, Zhang Xiao-wei, et al.. An efficient method for detecting slow-moving weak targets in sea clutter based on time-frequency iteration decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(6): 3659–3672. doi: 10.1109/TGRS.2012.2224665
    [5]
    许稼, 彭应宁, 夏香根, 等. 空时频检测前聚焦雷达信号处理方法[J]. 雷达学报, 2014, 3(2): 129–141. http://radars.ie.ac.cn/CN/abstract/abstract165.shtml

    Xu Jia, Peng Ying-ning, Xia Xiang-gen, et al.. Radar signal processing method of space-time-frequency focus-before-detects[J]. Journal of Radars, 2014, 3(2): 129–141. http://radars.ie.ac.cn/CN/abstract/abstract165.shtml
    [6]
    陈小龙, 关键, 黄勇, 等. 分数阶傅里叶变换在动目标检测和识别中的应用: 回顾和展望[J]. 信号处理, 2013, 29(1): 85–97. http://www.cnki.com.cn/Article/CJFDTOTAL-XXCN201301013.htm

    Chen Xiao-long, Guan Jian, Huang Yong, et al.. Application of fractional Fourier transform in moving target detection and recognition: Development and prospect[J]. Journal of Signal Processing, 2013, 29(1): 85–97. http://www.cnki.com.cn/Article/CJFDTOTAL-XXCN201301013.htm
    [7]
    Donoho David L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306. doi: 10.1109/TIT.2006.871582
    [8]
    李刚, 夏向根. 参数化稀疏表征在雷达探测中的应用[J]. 雷达学报, 2016, 5(1): 1–7. http://radars.ie.ac.cn/CN/abstract/abstract323.shtml

    Li Gang and Xia Xiang-gen. Parametric sparse representation and its applications to radar sensing[J]. Journal of Radars, 2016, 5(1): 1–7. http://radars.ie.ac.cn/CN/abstract/abstract323.shtml
    [9]
    焦李成, 杨淑媛, 刘芳, 等. 压缩感知回顾与展望[J]. 电子学报, 2011, 39(7): 1651–1662. http://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201107030.htm

    Jiao Licheng, Yang Shuyuan, Liu Fang, et al.. Development and prospect of compressive sensing[J]. Acta Electronic Sinica, 2011, 39(7): 1651–1662. http://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201107030.htm
    [10]
    宋杰, 何友, 关键. 一种双模杂波抑制的准自适应MTI系统[J]. 兵工学报, 2009, 30(5): 546–550. http://www.cnki.com.cn/Article/CJFDTOTAL-BIGO200905008.htm

    Song Jie, He You, and Guan Jian. A near adaptive MTI system for bimodal clutter suppression[J]. Acta Armamentarii, 2009, 30(5): 546–550. http://www.cnki.com.cn/Article/CJFDTOTAL-BIGO200905008.htm
    [11]
    马晓岩, 袁俊泉. 基于离散小波变换提高MTD检测性能的仿真分析[J]. 信号处理, 2001, 17(2): 148–151. http://www.cnki.com.cn/Article/CJFDTOTAL-XXCN200102009.htm

    Ma Xiao-yan and Yuan Jun-quan. Simulation analysis for MTD detectability improvement using the discrete wavelet transform (DWT)[J]. Journal of Signal Processing, 2001, 17(2): 148–151. http://www.cnki.com.cn/Article/CJFDTOTAL-XXCN200102009.htm
    [12]
    Guan Jian, Chen Xiao-long, et al.. Adaptive fractional Fourier transform-based detection algorithm for moving target in heavy sea clutter[J]. IET Radar, Sonar & Navigation, 2012, 6(5): 389–401. doi: 10.1049/iet-rsn.2011.0030
    [13]
    Chen Xiao-long, Guan Jian, Liu Ning-bo, et al.. Maneuvering target detection via Radon-fractional Fourier transform-based long-time coherent integration[J]. IEEE Transaction on Signal Processing, 2014, 62(4): 939–953. doi: 10.1109/TSP.2013.2297682
    [14]
    庞存锁. 基于离散多项式相位变换和分数阶傅里叶变换的加速目标检测算法[J]. 电子学报, 2012, 40(1): 184–188. http://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201201031.htm

    Pang Cun-suo. An accelerating target detection algorithm based on DPT and fractional Fourier transform[J]. Acta Electronic Sinica, 2012, 40(1): 184–188. http://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201201031.htm
    [15]
    Yonina C Eldar, Pavel Sidorenko, Dustin G Mixon, et al.. Sparse phase retrieval from short-time Fourier measurements[J]. IEEE Signal Processing Letters, 2015, 22(5): 638–642. doi: 10.1109/LSP.2014.2364225
    [16]
    Saad Qazi, Apostolos Georgakis, Lampros K Stergioulas, et al.. Interference suppression in the Wigner distribution using fractional Fourier transformation and signal synthesis[J]. IEEE Transactions on Signal Processing, 2007, 55(6): 3150–3154. doi: 10.1109/TSP.2007.893971
    [17]
    Barbarossa S. Analysis of multicomponent LFM signals by a combined Wigner-Hough transform[J]. IEEE Transactions on Signal Processing, 1995, 43(6): 1511–1515. doi: 10.1109/78.388866
    [18]
    Jérôme Gilles. Empirical wavelet transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16): 3999–4010. doi: 10.1109/TSP.2013.2265222
    [19]
    陶然, 邓兵, 王越. 分数阶傅里叶变换及其应用[M]. 北京: 清华大学出版社, 2009.

    Tao Ran, Deng Bing, and Wang Yue. Fractional Fourier Transform and Its Applications[M]. Beijing: Tsinghua University Press, 2009.
    [20]
    Feng Qiang and Li Bing-zhao. Convolution and correlation theorems for the two-dimensional linear canonical transform and its applications[J]. IET Signal Processing, 2016, 10(2): 125–132. doi: 10.1049/iet-spr.2015.0028
    [21]
    Liu Sheng-heng, Shan Tao, Tao Ran, et al.. Sparse discrete fractional Fourier transform and its applications[J]. IEEE Transactions on Signal Processing, 2014, 62(24): 6582–6595. doi: 10.1109/TSP.2014.2366719
    [22]
    Tao Ran, Li Xue-mei, Li Yan-lei, et al.. Time-delay estimation of chirp signals in the fractional Fourier domain[J]. IEEE Transactions on Signal Processing, 2009, 57(7): 2852–2855. doi: 10.1109/TSP.2009.2020028
    [23]
    Tao Ran, Zhang Feng, and Wang Yue. Fractional power spectrum[J]. IEEE Transactions on Signal Processing, 2008, 56(9): 4199–4206. doi: 10.1109/TSP.2008.925579
    [24]
    Tao Ran, Li Yan-lei, and Wang Yue. Short-time fractional Fourier transform and its applications[J]. IEEE Transactions on Signal Processing, 2010, 58(5): 2568–2580. doi: 10.1109/TSP.2009.2028095
    [25]
    沙学军, 史军, 张钦宇, 等. 分数傅里叶变换原理及其在通信系统中的应用[M]. 北京: 人民邮电出版社, 2013.

    Sha Xue-jun, Shi Jun, Zhang Qin-yu, et al.. Fractional Fourier Transform Theory and Its Applications in Communication Systems[M]. Beijing: Post & Telecom Press, 2013.
    [26]
    Liu Xiao-ping, Shi Jun, Xiang Wei, et al.. Sampling expansion for irregularly sampled signals in fractional Fourier transform domain[J]. Digital Signal Processing, 2014, 34: 74–81. doi: 10.1016/j.dsp.2014.08.004
    [27]
    Shi Jun, Xiang Wei, Liu Xiaoping, et al.. A sampling theorem for the fractional Fourier transform without band-limiting constraints[J]. Signal Processing, 2014, 98: 158–165. doi: 10.1016/j.sigpro.2013.11.026
    [28]
    Chen Xiao-long, Guan Jian, Bao Zhong-hua, et al.. Detection and extraction of target with micro-motion in spiky sea clutter via short-time fractional Fourier transform[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 1002–1018. doi: 10.1109/TGRS.2013.2246574
    [29]
    Xing Meng-dao, Su Jun-hai, Wang Gen-yuan, et al.. New parameter estimation and detection algorithm for high speed small target[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(1): 214–224. doi: 10.1109/TAES.2011.5705671
    [30]
    吴孙勇, 廖桂生, 朱圣棋, 等. 提高雷达机动目标检测性能的二维频率域匹配方法[J]. 电子学报, 2012, 40(12): 2415–2420. http://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201212010.htm

    Wu Sun-yong, Liao Gui-sheng, Zhu Sheng-qi, et al.. A new method for radar maneuvering target detection based on matched filtering in two-dimensional frequency domain[J]. Acta Electronica Sinica, 2012, 40(12): 2415–2420. http://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201212010.htm
    [31]
    Carlson B D, Evans E D, and Wilson S L. Search radar detection and track with the Hough transform. I. system concept[J]. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(1): 102–108. doi: 10.1109/7.250410
    [32]
    Yu Ji, Xu Jia, Peng Ying-ning, et al.. Radon-Fourier transform for radar target detection (III): Optimality and fast implementations[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2): 991–1004. doi: 10.1109/TAES.2012.6178044
    [33]
    Tao Ran, Zhang Nan, and Wang Yue. Analysing and compensating the effects of range and Doppler frequency migrations in linear frequency modulation pulse compression radar[J]. IET Radar, Sonar & Navigation, 2011, 5(1): 12–22. doi: 10.1049/iet-rsn.2009.0265
    [34]
    De Wind H J, Cilliers J E, and Herselman P L. Dataware: Sea clutter and small boat radar reflectivity databases[J]. IEEE Signal Processing Magazine, 2010, 27(2): 145–148. doi: 10.1109/MSP.2009.935415
    [35]
    Chen Xiao-long, Guan Jian, Liu Ning-bo, et al.. Detection of a low observable sea-surface target with micromotion via the Radon-linear canonical transform[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(7): 1225–1229. doi: 10.1109/LGRS.2013.2290024
    [36]
    Kong Ling-jiang, Li Xiao-long, Cui Guo-long, et al.. Coherent integration algorithm for a maneuvering target with high-order range migration[J]. IEEE Transactions on Signal Processing, 2015, 63(17): 4474–4486. doi: 10.1109/TSP.2015.2437844
    [37]
    Li Xiao-long, Cui Guo-long, Yi Wei, et al.. A fast maneuvering target motion parameters estimation algorithm based on ACCF[J]. IEEE Signal Processing Letters, 2015, 22(3): 265–269. doi: 10.1109/LSP.2014.2357681
    [38]
    Xu Jia, Xia Xiang-gen, Peng Shi-bao, et al.. Radar maneuvering target motion estimation based on generalized Radon-Fourier transform[J]. IEEE Transactions on Signal Processing, 2012, 60(12): 6190–6201. doi: 10.1109/TSP.2012.2217137
    [39]
    Chen Xiao-long, Huang Yong, Liu Ning-bo, et al.. Radon-fractional ambiguity function-based detection method of low-observable maneuvering target[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(2): 815–833. doi: 10.1109/TAES.2014.130791
    [40]
    Chen Xiao-long, Guan Jian, Huang Yong, et al.. Radon-linear canonical ambiguity function-based detection and estimation method for marine target with micromotion[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4): 2225–2240. doi: 10.1109/TGRS.2014.2358456
    [41]
    Chen Xiao-long, Guan Jian, Li Xiu-you, et al.. Effective coherent integration method for marine target with micromotion via phase differentiation and Radon-Lv’s distribution[J]. IET Radar, Sonar & Navigation (Special Issue: Micro-Doppler), 2015, 9(9): 1284–1295. doi: 10.1049/iet-rsn.2015.0100
    [42]
    陈小龙, 关键, 董云龙, 等. 稀疏域海杂波抑制与微动目标检测方法[J]. 电子学报, 2016, 44(4): 860–867. http://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201604015.htm

    Chen Xiao-long, Guan Jian, Dong Yun-long, et al.. Sea clutter suppression and micromotion target detection in sparse domain[J]. Acta Electronica Sinica, 2016, 44(4): 860–867. http://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201604015.htm
    [43]
    Faruk Uysal, Ivan Selesnick, Unnikrishna Pillai, et al.. Dynamic clutter mitigation using sparse optimization[J]. IEEE Aerospace and Electronic Systems Magazine, 2014, 29(7): 37–49. doi: 10.1109/MAES.2014.130137
    [44]
    Xu Jin, Wang Wei, Gao Jing-huai, et al.. Monochromatic noise removal via sparsity-enabled signal decomposition method[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3): 533–537. doi: 10.1109/LGRS.2012.2212271
    [45]
    罗倩. 基于稀疏表示的杂波建模和微弱运动目标探测[J]. 现代雷达, 2016, 38(2): 43–46. http://www.cnki.com.cn/Article/CJFDTOTAL-XDLD201602013.htm

    Luo Qian. Small moving target detection using sparse clutter modeling[J]. Modern Radar, 2016, 38(2): 43–46. http://www.cnki.com.cn/Article/CJFDTOTAL-XDLD201602013.htm
    [46]
    Gilbert A, Guha S, Indyk P, et al.. Near-optimal sparse Fourier representations via sampling[C]. Proceedings of the 34th ACM Symposium on Theory of Computing, New York, 2002: 152–161.
    [47]
    Yang Zhao-cheng, Li Xiang, Wang Hong-qiang, et al.. On clutter sparsity analysis in space-time adaptive processing airborne radar[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(5): 1214–1218. doi: 10.1109/LGRS.2012.2236639
    [48]
    Laura Anitori, Arian Maleki, Matern Otten, et al.. Design and analysis of compressed sensing radar detectors[J]. IEEE Transactions on Signal Processing, 2013, 61(4): 813–827. doi: 10.1109/TSP.2012.2225057
    [49]
    Marco F Duarte and Yonina C Eldar. Structured compressed sensing: From theory to applications[J]. IEEE Transactions on Signal Processing, 2011, 59(9): 4053–4085. doi: 10.1109/TSP.2011.2161982
    [50]
    Yang Jun-gang, Thompson John, Huang Xiao-tao, et al.. Random-frequency SAR imaging based on compressed sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(2): 983–994. doi: 10.1109/TGRS.2012.2204891
    [51]
    方明, 戴奉周, 刘宏伟, 等. 基于联合稀疏恢复的宽带雷达动目标检测方法[J]. 电子与信息学报, 2015, 37(12): 2977–2983. http://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201512027.htm

    Fang Ming, Dai Feng-zhou, Liu Hong-wei, et al.. Detection of moving targets for wideband radar based on joint-sparse recovery[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2977–2983. http://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201512027.htm
    [52]
    朱厦, 李彦鹏, 黎湘, 等. 基于信号稀疏表示的Chirp信号参数估计方法[J]. 现代雷达, 2008, 30(4): 59–63. http://www.cnki.com.cn/Article/CJFDTOTAL-XDLD200804017.htm

    Zhu Xia, Li Yan-peng, Li Xiang, et al.. A new method for parameter estimation of chirp signal based on sparse signal representation[J]. Modern Radar, 2008, 30(4): 59–63. http://www.cnki.com.cn/Article/CJFDTOTAL-XDLD200804017.htm
    [53]
    余付平, 冯有前, 高大化, 等. 基于稀疏分解的雷达信号抗噪声干扰方法研究[J]. 系统工程与电子技术, 2011, 33(8): 1765–1769. http://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201108019.htm

    Yu Fu-ping, Feng You-qian, Gao Da-hua, et al.. Research on anti-noise jamming of radar signals based on sparse decomposition[J]. Systems Engineering and Electronics, 2011, 33(8): 1765–1769. http://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201108019.htm
    [54]
    Victor C Chen, Fayin Li, Shen-Shyang Ho, et al.. Micro-Doppler effect in radar: Phenomenon, model, and simulation study[J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(1): 2–21. doi: 10.1109/TAES.2006.1603402
    [55]
    Victor C Chen, David Tahmoush, and William J Miceli. Radar Micro-Doppler Signature: Processing and Applications[M]. UK: IET, 2014.
    [56]
    Li Gang and Pramod K Varshney. Micro-Doppler parameter estimation via parametric sparse representation and pruned orthogonal matching pursuit[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(12): 1939–1404.
    [57]
    Zhu Sheng-qi, Liao Gui-sheng, Qu Yi, et al.. Ground moving targets imaging algorithm for synthetic aperture radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(1): 462–477. doi: 10.1109/TGRS.2010.2053848
    [58]
    贺思三, 赵会宁, 张永顺. 基于延迟共轭相乘的弹道目标平动补偿[J]. 雷达学报, 2014, 3(5): 505–510. http://radars.ie.ac.cn/CN/abstract/abstract204.shtml

    He Si-san, Zhao Hui-ning, and Zhang Yong-shun. Translational motion compensation for ballistic targets based on delayed conjugated multiplication[J]. Journal of Radars, 2014, 3(5): 505–510. http://radars.ie.ac.cn/CN/abstract/abstract204.shtml
    [59]
    罗迎, 张群, 王国正, 等. 基于复图像OMP分解的宽带雷达微动特征提取方法[J]. 雷达学报, 2012, 1(4): 361–369. http://radars.ie.ac.cn/CN/abstract/abstract49.shtml

    Luo Ying, Zhang Qun, Wang Guo-zheng, et al.. Micro-motion signature extraction method for wideband radar based on complex image OMP decomposition[J]. Journal of Radars, 2012, 1(4): 361–369. http://radars.ie.ac.cn/CN/abstract/abstract49.shtml
    [60]
    Hadi Zayyani and Babaie-Zadeh M. Approximated Cramér-Rao bound for estimating the mixing matrix in the two-sensor noisy Sparse Component Analysis (SCA)[J]. Digital Signal Processing, 2013, 23: 771–779. doi: 10.1016/j.dsp.2012.12.016
    [61]
    Ali Gholami. Sparse time-frequency decomposition and some applications[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(6): 3598–3604. doi: 10.1109/TGRS.2012.2220144
    [62]
    Patrick Flandrin and Pierre Borgnat. Time-frequency energy distributions meet compressed sensing[J]. IEEE Transactions on Signal Processing, 2010, 58(6): 2974–2982. doi: 10.1109/TSP.2010.2044839
    [63]
    陈沛, 赵拥军, 刘成城. 基于稀疏时频分解的盲波束形成算法[J]. 电子与信息学报, 2016, 38(12): 3078–3084. http://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201612014.htm

    Chen Pei, Zhao Yong-jun, and Liu Cheng-cheng. Blind beamforming algorithm based on sparse time-frequency decomposition[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3078–3084. http://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201612014.htm
    [64]
    陈小龙, 关键, 于晓涵, 等. 基于短时稀疏时频分布的雷达目标微动特征提取及检测方法[J]. 电子与信息学报, 已录用.

    Chen Xiao-long, Guan Jian, Yu Xiao-han, et al.. Radar micro-Doppler signature extraction and detection via short-time sparse time-frequency distribution[J]. Journal of Electronics & Information Technology, Accepted.
    [65]
    Gilbert A, Indyk P, Iwen M, et al.. Recent developments in the sparse Fourier transform: A compressed Fourier transform for big data[J]. IEEE Signal Processing Magazine, 2014, 31(5): 91–100. doi: 10.1109/MSP.2014.2329131
    [66]
    Gotz E Pfander and Holger Rauhut. Sparsity in time-frequency representations[J]. Journal of Fourier Analysis and Applications, 2010, 16(2): 233–260. doi: 10.1007/s00041-009-9086-9
    [67]
    Nicholas Whitelonis and Hao Ling. Application of a compressed sensing based time-frequency distribution for radar signature analysis[C]. 2012 IEEE Antennas and Propagation Society International Symposium and USNC/URSI National Radio Science Meeting, 2012: 1–2.
    [68]
    Branka Jokanovic, Moeness Amin, and Srdjan Stankovic. Instantaneous frequency and time-frequency signature estimation using compressive sensing[C]. Proceedings of the SPIE 8714, Radar Sensor Technology XVII 871418, 2013. DOI: 10.1117/12.2016636.
    [69]
    焦李成, 赵进, 杨淑媛, 等. 稀疏认知学习、计算与识别的研究进展[J]. 计算机学报, 2016, 39(4): 835–851. doi: 10.11897/SP.J.1016.2016.00835

    Jiao Li-cheng, Zhao Jin, Yang Shu-yuan, et al.. Research advances on sparse cognitive learning, computing and recognition[J]. Chinese Journal of Computers, 2016, 39(4): 835–851. doi: 10.11897/SP.J.1016.2016.00835
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(3765) PDF downloads(1875) Cited by()
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return