Automatic target classification for low-frequency anti-submarine warfare sonars

摘要: Autonomous anti-submarine warfare (ASW) sonars require robust automatic target classification algorithms. In conventional systems with human operators, the main role of such algorithms is to simplify work sonar operator, while in autonomous systems, crucial for operative value systems. The emergence underwater vehicle (AUV), coupled ongoing increase computational power allowing more advanced real-time processing, has increased interest naval community. Detailed knowledge environment and an acoustic model may be used estimate probability that contacts are generated due signal processing induced phenomenon called false alarm rate inflation (FARI). This a often encountered littorals presence bathymetric features as sea mounts ridges. this paper, we propose combining FARI information track information, using two different machine learning techniques, k-Nearest neighbours ID3.