Underwater Acoustic Target Recognition Using Software Defined Radio - Pehlivan, Adil Ugur; Ilhan, Haci (IEEE, 2023)
One of the important results of ship movement is acoustics noise which contains a lot of ship attributes. With developing technology, the classification of targets problem remains important. Recognition of ships is possible using ships' acoustic trace data. Determining the appropriate method with deep learning methods of acoustic target traces is very important for fast division. The collection and processing of acoustic data from the sea environment and evaluation of the processed data have operational difficulties due to environmental conditions. In this paper, to overcome the aforementioned difficulties, it aims to transmit the acoustic data received with the buoy into shore station, classify the transmitted data, and provide information about the target to the systems in the shore station. It is aimed to obtain a suitable deep learning method for the shipsear data sets used in previous studies on the shore station.
Keywords: Deep Learning; Software Defined Radio; Underwater Acoustic Target Recognition; Underwater Acoustics.; Computer Science; Engineering