Active sonar target imaging and classification system

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Active sonar target imaging and classification system

Burton, L.L. / Hung Lai

Active sonar classification of suspended, bottomed, and buried mines is very important in littoral warfare. New active biosonar models based on bat-like range profiling and dolphin-like image construction may reduce the problem of false alarms. The performance of one such biosonar algorithm, the spectrogram correlation and transformation (SCAT) model developed at Brown University, has been compared with the performance of a standard matched filter on a data set obtained from NSWC Coastal Systems Station, Dahlgren Division. This data set contains echoes form six objects: two mine-like objects, a water-filled 50-gallon drum, a rough limestone rock, a smooth granite rock, and a water-saturated log. Three neural network architectures (multilayer perceptron, ellipsoidal basis function, and hierarchical types) were used as classifiers. Discrimination was performed between man-made and non-man-made objects, between mine-like and non-mine-like objects, among the three types of man-made objects, and among the six different test objects using single pings, multiple ping fusion, fusion of the results from different algorithms, and a combination of algorithm fusion and multiple ping fusion.