Radar target identification using an eigen-image approach

Novak, Leslie M.; Owirka, G.J.

doi:10.1109/nrc.1994.328111

Cited by 24 publications

(14 citation statements)

References 4 publications

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“…To show the effectiveness of the proposed method further, we evaluate the performance of OMMPS and OKMMPS compared with MMC [38], PCA [20], LDA [21], KPCA [22], and KFDA [23] Figure 4 shows the average rates of seven methods versus SNR. Some interesting observations can be seen from Fig.…”

Section: Performance Comparisonmentioning

confidence: 99%

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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: Performance Comparisonmentioning

confidence: 99%

“…For example, the principal component analysis (PCA) can preserve the large variance directions [20]. The linear discriminant analysis (LDA) is able to maximize the between-class distance and minimize the withinclass distance simultaneously [21].…”

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 experiment, we compare the recognition performance of OT, ES [5], CS [8] and LPP [11]. In OT method, we select the optimal clustering centers as previous experiment.…”

Section: Comparison Of Recognition Performancementioning

confidence: 99%

“…For instance, L. M. Novak et al presented a feature extraction method for radar target recognition based on Eigen subspace (ES) [5]. A. Quinquis et al applied ES method to increase the resolution of HRRP for improving the performance of target recognition [6].…”

Section: Introductionmentioning

confidence: 99%

Radar HRRP target recognition based on optimal transformation

Zhou

2013

Proceedings of 2013 3rd International Conference on Computer Science and Network Technology

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A novel radar target recognition method using high-resolution range profile (HRRP), called as optimal transformation (OT), is proposed in this paper. The aim of this approach is to increase the distance between the different classes, and reduce the distance between the same class. Firstly, OT enhances the between-class difference by increasing the distance between clustering centers. Then, OT reduces the distance between the same class by seeking a transformation matrix that can preserve the local structure of the within-class optimally Thus, OT has better performance of recognition than the other methods. Experimental results on the measured data of three airplane targets show that the effectiveness of proposed method.

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“…For example, Novak and Owirka (1994) proposed an Eigen subspace (ES) feature extraction method for radar target recognition. Quinquis et al (2001) used ES techniques to enhance the resolution of HRRP for improving the target recognition performance.…”

Section: Introductionmentioning

confidence: 99%