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Erschienen in:
2017 | OriginalPaper | Buchkapitel
2C-SVM Based Radar Detectors in Gaussian and K-Distributed Real Interference
verfasst von : David Mata-Moya, Maria-Pilar Jarabo-Amores, Manuel Rosa-Zurera, Javier Rosado-Sanz, Nerea del-Rey-Maestre
Erschienen in: Advances in Computational Intelligence
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Abstract
This paper tackles the design and evaluation of cost sensitive Support Vector Machine (2C-SVM) based radar detectors in presence of Gaussian and K-Distributed clutter. 2C-SVM based solutions are able to approximate the Neyman-Pearson detector for a specific false alarm rate (\(P_{FA}\)). Real data acquired in different wind conditions by a coherent, pulsed and X-Band radar were considered. A statistical analysis is carried out to design the 2C-SVM for detecting targets with unknown parameters in Gaussian and non-Gaussian interference. A grid search of the best training parameters to approximate the pair detection probability (\(P_D\)) and \(P_{FA}\) of the NP detector is required. Results prove the capability of the 2C-SVM based detectors to maximize the \(P_D\) for a desired \(P_{FA}\) independently of the detection problem likelihood functions.