Wreck finding and classifying with a sonar filter

Kenneth I. Agehed; Mary Lou Padgett; Vlatko Becanovic; C. Bornich; Age J. Eide; Per Engman; O. Globoden; Thomas Lindblad; K. Lodgberg; Karina E. Waldemark

doi:10.1117/12.343054

22 March 1999 Wreck finding and classifying with a sonar filter

Kenneth I. Agehed, Mary Lou Padgett, Vlatko Becanovic, C. Bornich, Age J. Eide, Per Engman, O. Globoden, Thomas Lindblad, K. Lodgberg, Karina E. Waldemark

Author Affiliations +

Proceedings Volume 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks; (1999) https://doi.org/10.1117/12.343054
Event: Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, 1998, Stockholm, Sweden

Abstract

Sonar detection and classification of sunken wrecks and other objects is of keen interest to many. This paper describes the use of neural networks (NN) for locating, classifying and determining the alignment of objects on a lakebed in Sweden. A complex program for data preprocessing and visualization was developed. Part of this program, The Sonar Viewer, facilitates training and testing of the NN using (1) the MATLAB Neural Networks Toolbox for multilayer perceptrons with backpropagation (BP) and (2) the neural network O-Algorithm (OA) developed by Age Eide and Thomas Lindblad. Comparison of the performance of the two neural networks approaches indicates that, for this data BP generalizes better than OA, but use of OA eliminates the need for training on non-target (lake bed) images. The OA algorithm does not work well with the smaller ships. Increasing the resolution to counteract this problem would slow down processing and require interpolation to suggest data values between the actual sonar measurements. In general, good results were obtained for recognizing large wrecks and determining their alignment. The programs developed a useful tool for further study of sonar signals in many environments. Recent developments in pulse coupled neural networks techniques provide an opportunity to extend the use in real-world applications where experimental data is difficult, expensive or time consuming to obtain.

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Kenneth I. Agehed, Mary Lou Padgett, Vlatko Becanovic, C. Bornich, Age J. Eide, Per Engman, O. Globoden, Thomas Lindblad, K. Lodgberg, and Karina E. Waldemark "Wreck finding and classifying with a sonar filter", Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); https://doi.org/10.1117/12.343054

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KEYWORDS

Neural networks

Visualization

Software development

Data visualization

MATLAB

Neurons

Visual analytics

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