Non-cooperative target recognition in the frequency domain

Wong, S.K.

doi:10.1049/ip-rsn:20040197

Cited by 27 publications

(20 citation statements)

References 1 publication

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“…Y. Shi et al [10] use a novel neural network classifier for HRRP recognition. S. K. Wong [11] presents a feature selection method in frequency domain. D. E. Nelson et al [12] study a new iterated wavelet feature for HRRP classification.…”

Section: Introductionmentioning

confidence: 99%

Orthogonal maximum margin projection subspace for radar target HRRP recognition

Zhou

2016

J Wireless Com Network

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In this paper, a novel target recognition method, namely orthogonal maximum margin projection subspace (OMMPS), is proposed for radar target recognition using high-resolution range profile (HRRP). The core of OMMPS is to maximize the between-class margin by increasing the between-class scatter distance and reducing the within-class scatter distance simultaneously. By introducing the nonlinear mapping function, we also derive the kernel version of OMMPS, namely orthogonal kernel maximum margin projection subspace (OKMMPS). Compared with maximum margin criterion (MMC) method, OMMPS are optimal in meaning of maximum margin due that the coordinate axes of OMMPS are obtained sequentially by solving the constrained optimization problem, thus improves the recognition performance. In addition, the number of efficient features for OMMPS is not limited by the number of pattern classes, and the appropriate features can still be obtained for separating the classes, even in high-dimensional space with only a few classes. Moreover, the coordinate axes of OMMPS are mutually orthogonal, and the features extracted by OMMPS reduce the redundancy. The extensive experimental results show that the proposed method has better recognition performance than the other methods such as MMC and LDA.

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Section: Introductionmentioning

confidence: 99%

Orthogonal maximum margin projection subspace for radar target HRRP recognition

Zhou

2016

J Wireless Com Network

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“…These information is very useful for target recognition, thus recently the researchers have focused their interest on radar target recognition using HRRP (Li and Yang, 1993;Zyweck and Bogner, 1996;Eom and Chellappa, 1997;Rosenbach and Schiller, 2000;Jacobs and Sullivan, 2000;Huther et al, 2001;Shaw et al, 2000;Wu et al, 2002;Du et al, 2006;Suvorova and Schroeder, 2002;Kim et al, 2002;Zwart et al, 2003;Nelson et al, 2003;Wong, 2004;Shi and Zhang, 2001;Liao et al, 2002;Mitchell and Westerkamp, 1999). Invariant features for HRRP were studied in Suvorova and Schroeder (2002), Kim et al (2002), Zwart et al (2003).…”

Section: Introductionmentioning

confidence: 99%

“…Nelson et al (2003)proposed a new iterated wavelet feature of HRRP. Wong (2004) investigated feature selection in frequency domain. Shi and Zhang (2001) presented a new neural network classifier.…”

Section: Introductionmentioning

confidence: 99%

Radar target recognition based on fuzzy optimal transformation using high-resolution range profile

Zhou¹,

Shen²,

Yang³

2013

Pattern Recognition Letters

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“…D. E. Nelson et al proposed a new feature selection method for HRRP [5]. S. K. Wong applied the features in frequency domain for non-cooperative target recognition [6]. L. Du et al studied the two-distribution compounded statistical model for radar HRRP target recognition [7].…”

Section: Introductionmentioning

confidence: 99%

Radar Target Recognition Based on Kernel Projection Vector Space Using High-resolution Range Profile

Zhou

2013

2013 Third International Conference on Intelligent System Design and Engineering Applications

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In this paper, a novel approach, namely kernel projection vector space (KPVS), is proposed for radar target recognition using high-resolution range profile (HRRP). First, the HRRP samples are mapped into a high-dimensional feature space using nonlinear mapping. Second, the kernel projection vectors, are obtained by kernel discriminant analysis. Then, for each class, the kernel projection vector space is formed using all the training kernel projection vectors of class. Finally, the minimum hyperplane distance classifier (MHDC) is used for classification. The aim of KPVS method is to represent the feature area of target using kernel projection vector space, and effectively measure the distance between the test HRRP and feature area via minimum hyperplane distance (MHD) metric. The experimental results of measured data show that the proposed method has better performance of recognition than KPCA and KFDA.

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Non-cooperative target recognition in the frequency domain

Cited by 27 publications

References 1 publication

Orthogonal maximum margin projection subspace for radar target HRRP recognition

Orthogonal maximum margin projection subspace for radar target HRRP recognition

Radar target recognition based on fuzzy optimal transformation using high-resolution range profile

Radar Target Recognition Based on Kernel Projection Vector Space Using High-resolution Range Profile

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