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- W2616181900 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." @default.
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- W2616181900 date "2017-01-01" @default.
- W2616181900 modified "2023-09-27" @default.
- W2616181900 title "2C-SVM Based Radar Detectors in Gaussian and K-Distributed Real Interference" @default.
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- W2616181900 doi "https://doi.org/10.1007/978-3-319-59153-7_23" @default.
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