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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Vydáno v:IET radar, sonar & navigation Ročník 10; číslo 9; s. 1594 - 1602
Hlavní autoři: Xiong, Wei, Zhang, Gong, Liu, Su, Yin, Jiejun
Médium: Journal Article
Jazyk:angličtina
Vydáno: The Institution of Engineering and Technology 01.12.2016
Témata:
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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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ObjectType-Feature-2
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ISSN:1751-8784
1751-8792
DOI:10.1049/iet-rsn.2015.0540