2020-10-081.01837428FFIForsvarets forskningsinstituttNorwegian Defence Research EstablishmentFFI2020-04-012020-10-06T09:21:10Z2020-10-06T09:23:27Z22784AkhtarJabran7428Forsvarets forskningsinstituttNorwegian Defence Research EstablishmentForsvarets forskingsinstituttFFINONorgeNorwayTIDSSKRIFTPUBLTidsskriftspublikasjonJournal publicationARTIKKELVitenskapelig artikkelAcademic article22784false1AkhtarJabran74281100ForsvarssystemerDefence Systems7428Forsvarets forskningsinstituttNorwegian Defence Research EstablishmentForsvarets forskingsinstituttFFINONorgeNorwayINSTITUTTInstituttsektorenInstitute SectorForsvarets forskningsinstitutt|ForsvarssystemerNorwegian Defence Research Establishment|Defence Systems|FORFATTERForfatterAuthorENEngelskEnglishTraining of Neural Network Target Detectors Mentored by SO-CFAR20202020ENEngelskEnglishA desirable objective in radar detection theory is the ability to detect and recognize targets in intricate scenarios such as in the presence of clutter or multiple closely spaced targets. Herein we propose the use of artificial neural networks for radar target detection where Smallest Of (SO)-CFAR detector is used as the basis for neural network training. The SO-CFAR detector has exceptional good detectional capabilities, however, suffers from a very high false alarm rate and has therefore only been given limited attention in the literature. We show that by appropriately training a neural network on SO-CFAR detections it is possible to significantly lower the false alarm rate with only marginal decrease in probability of detection.true20203Artikkel i periodika og serierArticle in Periodical and series1019416European Signal Processing Conference2076-14651Kvalitetsnivå 1Quality level 11original1837428.pdf3477902020-10-08704d11a7b3c243ff34f455d7e060502eCaroline Johansen Musæus, FFI1159693European Signal Processing Conference2076-1465ENEngelskEnglishINTERNASJInternasjonaltInternationalhttps://www.eurasip.org/index.php?option=com_content&view=article&id=80&Itemid=891Kvalitetsnivå 1Quality level 1202015221526