Beamforming using support vector machines

Ramón, Manel Martínez; Xu, Nan; Christodoulou, C.G.

doi:10.1109/lawp.2005.860196

Cited by 59 publications

(17 citation statements)

References 5 publications

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“…Since only a simple classifier (the minimum distance classifier) is adopted in this letter, more complicated classifiers like neural networks (NNs) [16] and support vector machine (SVM) [17] can be applied to further improve the classification results of the proposed method.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Neighborhood-Preserving Discriminant Projection Method for HRRP-Based Radar Target Recognition

Zhang

Ding

Fan

et al. 2015

Antennas Wirel. Propag. Lett.

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A new manifold learning algorithm named adaptive neighborhood preserving discriminant projection method is proposed for the feature extraction of high-range resolution profile (HRRP)-based radar target recognition. By utilizing the objective functions of both neighborhood-preserving projection (NPP) and adaptive maximum margin criterion (AMMC), the proposed method can not only preserve the neighborhood structure of original data in the dimensionality reduced space, but also exhibit good classification performance. The proposed method is applied to the feature extraction of HRRP-based radar target recognition. Numerical experiments show that the proposed method can effectively reduce the dimensionality of HRRP and give satisfactory recognition rate.Index Terms-High-range resolution profile (HRRP), manifold learning, radar target recognition.

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Section: Discussionmentioning

confidence: 99%

Adaptive Neighborhood-Preserving Discriminant Projection Method for HRRP-Based Radar Target Recognition

Zhang

Ding

Fan

et al. 2015

Antennas Wirel. Propag. Lett.

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“…SVM has demonstrated superior results in various classification and pattern recognition problems [35,36]. Furthermore, for several pattern classification applications, SVM has already been proven to provide better generalization performance than conventional techniques especially when the number of input variables is large [37,38]. With this purpose in mind, we evaluated the SVM for our fused feature vector.…”

Section: Support Vector Machinementioning

confidence: 99%

A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification

Gawande¹,

Zaveri

Kapur

2013

Applied Computational Intelligence and Soft Computing

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Recent times witnessed many advancements in the field of biometric and ultimodal biometric fields. This is typically observed in the area, of security, privacy, and forensics. Even for the best of unimodal biometric systems, it is often not possible to achieve a higher recognition rate. Multimodal biometric systems overcome various limitations of unimodal biometric systems, such as nonuniversality, lower false acceptance, and higher genuine acceptance rates. More reliable recognition performance is achievable as multiple pieces of evidence of the same identity are available. The work presented in this paper is focused on multimodal biometric system using fingerprint and iris. Distinct textual features of the iris and fingerprint are extracted using the Haar waveletbased technique. A novel feature level fusion algorithm is developed to combine these unimodal features using the Mahalanobis distance technique. A support-vector-machine-based learning algorithm is used to train the system using the feature extracted. The performance of the proposed algorithms is validated and compared with other algorithms using the CASIA iris database and real fingerprint database. From the simulation results, it is evident that our algorithm has higher recognition rate and very less false rejection rate compared to existing approaches.

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“…The theory of SVR is initially introduced by Vapnik [17] based on the principle of structural risk minimization and is applied in a number of communications problems such as wireless sensor networks [18] and digital image processing [19]. Due to its improved generalization capabilities, it is widely employed in the array signal processing problems to control the sidelobe levels [20][21][22][23][24] and the antenna array synthesis problems [25][26][27][28]. The SVR-based beamforming algorithm is initially proposed [20] to improve the performance of MVDR.…”

Section: Introductionmentioning

confidence: 99%

“…Due to its improved generalization capabilities, it is widely employed in the array signal processing problems to control the sidelobe levels [20][21][22][23][24] and the antenna array synthesis problems [25][26][27][28]. The SVR-based beamforming algorithm is initially proposed [20] to improve the performance of MVDR. It utilizes the Vapnik insensitive function to serve as a penalty term to penalize the sidelobe level.…”

Section: Introductionmentioning

confidence: 99%

SVR-CMT Algorithm for Null Broadening and Sidelobe Control

Liu¹,

Wu²,

Duan³

et al. 2018

PIER

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Minimum variance distortionless response (MVDR) beamformer is an adaptive beamforming technique that provides a method for separating the desired signal from interfering signals. Unfortunately, the MVDR beamformer may have unacceptably low nulling level and high sidelobes, which may lead to significant performance degradation in the case of unexpected interfering signals such as the rapidly moving jammer environments. Via support vector machine regression (SVR), a novel beamforming algorithm (named as SVR-CMT algorithm) is presented for controlling the sidelobes and the nullling level. In the proposed method, firstly, the covariance matrix is tapered based on Mailloux covariance matrix taper (CMT) procedure to broaden the width of nulls for interference signals. Secondly, the equality constraints are modified into inequality constraints to control the sidelobe level. By the ε-insensitive loss function for the sidelobe controller, the modified beamforming optimization problem is formulated as a standard SVR problem so that the weight vector can be obtained effectively. Compared with the previous works, the proposed SVR-CMT method provides better beamforming performance. For instance, (1) it can effectively control the sidelobe and nullling level, (2) it can improve the output signal-to-interference-and-noise ratio (SINR) performance even if the direction-of-arrival (DOA) errors exist. Simulation results demonstrate the efficiency of the presented approach.

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Beamforming using support vector machines

Cited by 59 publications

References 5 publications

Adaptive Neighborhood-Preserving Discriminant Projection Method for HRRP-Based Radar Target Recognition

Adaptive Neighborhood-Preserving Discriminant Projection Method for HRRP-Based Radar Target Recognition

A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification

SVR-CMT Algorithm for Null Broadening and Sidelobe Control

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