Beamforming using support vector machines

Support vector machines (SVMs) have improved generalization performance over other classical optimization techniques. Here, we introduce an SVM-based approach for linear array processing and beamforming. The development of a modified cost function is presented and it is shown how it can be applied t...

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Bibliographic Details
Published inIEEE antennas and wireless propagation letters Vol. 4; pp. 439 - 442
Main Authors Ramon, M.M., Nan Xu, Christodoulou, C.G.
Format Journal Article
LanguageEnglish
Published New York IEEE 2005
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Antenna arrays
Antennas
Array signal processing
Beamforming
complex support vector machines
Constraint optimization
Cost function
Linear arrays
Mean square error methods
Minimization
Noise robustness
Optimization
Performance enhancement
Signal processing
Signal processing algorithms
Support vector machines
support vector machines (SVMs)
Working environment noise
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Summary:Support vector machines (SVMs) have improved generalization performance over other classical optimization techniques. Here, we introduce an SVM-based approach for linear array processing and beamforming. The development of a modified cost function is presented and it is shown how it can be applied to the problem of linear beamforming. Finally, comparison examples are included to show the validity of the new minimization approach.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1536-1225
1548-5757
DOI:10.1109/LAWP.2005.860196