Sonar discrimination of cylinders from different angles using neural networks
نویسندگان
چکیده
This paper describes an underwater object discrimination system applied to recognize cylinders of various compositions from diierent angles. The system is based on a new combination of simulated dolphin clicks, simulated auditory lters and artiicial neural networks. The model demonstrates its potential on real data collected from four diierent cylinders in an environment where the angles were controlled in order to evaluate the models capabilities to recognize cylinders independent of angles.
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