Beamforming using support vector machines

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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Veröffentlicht in:IEEE antennas and wireless propagation letters 2005, Vol.4, p.439-442
Hauptverfasser: Ramon, M.M., Nan Xu, Christodoulou, C.G.
Format: Artikel
Sprache:eng
Schlagworte:
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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Beschreibung
Zusammenfassung: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.
ISSN:1536-1225
1548-5757
DOI:10.1109/LAWP.2005.860196