Multiscale kernel sparse coding-based classifier for HRRP radar target recognition

With the combined multiscale Gaussian kernel and Morlet wavelet kernel, two multiscale kernel sparse coding-based classifiers (MKSCCs) are proposed for radar target recognition using high-resolution range profiles (HRRPs). The kernel trick can make samples more clustered in higher-dimensional space....

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Veröffentlicht in:	IET radar, sonar & navigation Jg. 10; H. 9; S. 1594 - 1602
Hauptverfasser:	Xiong, Wei, Zhang, Gong, Liu, Su, Yin, Jiejun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 01.12.2016
Schlagworte:	Classifiers combined multiscale Gaussian kernel Gaussian processes higher‐dimensional space clustering high‐resolution range profile HRRP HRRP radar target recognition image classification image coding Kernels MKSCC Morlet wavelet Morlet wavelet kernel multiscale kernel sparse coding‐based classification Navigation object recognition radar imaging radar resolution Radar targets Recognition Research Article Robustness Sonar wavelet transforms object recognition wavelet transforms image classification multiscale kernel sparse coding-based classification HRRP high-resolution range profile combined multiscale Gaussian kernel HRRP radar target recognition Morlet wavelet kernel radar resolution MKSCC Gaussian processes higher-dimensional space clustering radar imaging image coding
ISSN:	1751-8784, 1751-8792
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Abstract	With the combined multiscale Gaussian kernel and Morlet wavelet kernel, two multiscale kernel sparse coding-based classifiers (MKSCCs) are proposed for radar target recognition using high-resolution range profiles (HRRPs). The kernel trick can make samples more clustered in higher-dimensional space. Moreover, the multiscale kernels at different scales have advantages of good generalisation and primary signature capturing ability for target's HRRP, which are helpful to improve the target recognition accuracy and robustness of MKSCC further. Numerous experiments are conducted on five types of ground vehicles’ HRRP data and the authors also make comparisons with the KSCC and some related recognition methods. The results demonstrate the effectiveness of the proposed method.
AbstractList	With the combined multiscale Gaussian kernel and Morlet wavelet kernel, two multiscale kernel sparse coding-based classifiers (MKSCCs) are proposed for radar target recognition using high-resolution range profiles (HRRPs). The kernel trick can make samples more clustered in higher-dimensional space. Moreover, the multiscale kernels at different scales have advantages of good generalisation and primary signature capturing ability for target's HRRP, which are helpful to improve the target recognition accuracy and robustness of MKSCC further. Numerous experiments are conducted on five types of ground vehicles’ HRRP data and the authors also make comparisons with the KSCC and some related recognition methods. The results demonstrate the effectiveness of the proposed method.
Author	Zhang, Gong Liu, Su Yin, Jiejun Xiong, Wei
Author_xml	– sequence: 1 givenname: Wei surname: Xiong fullname: Xiong, Wei organization: 2Leihua Electronic Technology Research Institute, Aviation Industry Corporation of China, Wuxi 214063, People's Republic of China – sequence: 2 givenname: Gong surname: Zhang fullname: Zhang, Gong email: gzhang@nuaa.edu.cn organization: 1Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China – sequence: 3 givenname: Su surname: Liu fullname: Liu, Su organization: 1Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China – sequence: 4 givenname: Jiejun surname: Yin fullname: Yin, Jiejun organization: 1Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China
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Cites_doi	10.1109/MSP.2010.939537 10.1109/TSP.2013.2274276 10.1016/S1007-0214(09)70099-9 10.1109/TSP.2007.912283 10.1109/TSP.2011.2141664 10.1007/s11432-008-0087-0 10.1109/78.330373 10.1007/s10444-004-7622-3 10.1109/TPAMI.2007.70786 10.1137/S1064827596304010 10.1049/iet-rsn.2014.0113 10.1111/j.2517-6161.1996.tb02080.x 10.1049/iet-rsn.2012.0034 10.1109/72.914517 10.1109/TAES.2010.5595601 10.1016/S1000-9361(09)60254-5 10.1049/iet-rsn:20050119 10.1109/TGRS.2010.2048575 10.1109/TGRS.2010.2051231 10.1109/TSP.2010.2088391 10.1109/62.839633 10.1109/TSP.2005.849161 10.1109/TSP.2006.873534 10.1109/TSP.2012.2191965
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Keywords	object recognition wavelet transforms image classification multiscale kernel sparse coding-based classification HRRP high-resolution range profile combined multiscale Gaussian kernel HRRP radar target recognition Morlet wavelet kernel radar resolution MKSCC Gaussian processes higher-dimensional space clustering radar imaging image coding
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Snippet	With the combined multiscale Gaussian kernel and Morlet wavelet kernel, two multiscale kernel sparse coding-based classifiers (MKSCCs) are proposed for radar... With the combined multiscale Gaussian kernel and Morlet wavelet kernel, two multiscale kernel sparse coding‐based classifiers (MKSCCs) are proposed for radar...
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SubjectTerms	Classifiers combined multiscale Gaussian kernel Gaussian processes higher‐dimensional space clustering high‐resolution range profile HRRP HRRP radar target recognition image classification image coding Kernels MKSCC Morlet wavelet Morlet wavelet kernel multiscale kernel sparse coding‐based classification Navigation object recognition radar imaging radar resolution Radar targets Recognition Research Article Robustness Sonar wavelet transforms
Title	Multiscale kernel sparse coding-based classifier for HRRP radar target recognition
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