Multichannel Detection and Acoustic Color Based Classification of Underwater UXO in Sonar
Technical Report,01 Mar 2014,01 Jun 2015
Colorado State University Fort Collins United States
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The objective of this work is the development of efficient signal processing techniques for the detection and classification of military munitions in shallow underwater environments using data collected from synthetic aperture sonar SAS systems. In this final report we first address the problem of detecting the presence of underwater munitions using the multichannel coherence analysis framework. Our detection hypothesis is that the presence of munitions in the sonar backscatter collected from a hydrophone array will lead to higher levels of coherence compared to the backscatter from the seafloor alone. This method has been found to produce excellent detection performance on other sonar datasets. Here, detection results are presented on a sonar dataset which was collected in a relatively controlled and clutter-free environment. Results are presented using standard performance metrics such as probability of detection Pd, probability of false alarm Pfa, and Receiver Operating Characteristic ROC curve characteristics. The goal of the second part of this work is to develop a robust target classification method that can be applied to the detected contacts to discriminate munitions from non-hostile man made objects and competing clutter. This method is developed based upon the Matched Subspace Classifier MSC using multidimensional Acoustic Color AC data extracted from the raw sonar returns. Scattering models developed by APL-UW were acquired to generate the required training dataset for various UXO and non-UXO objects. This was done owing to the fact that actual sonar data from a wide range of UXO and non-UXO objects is scarce in realistic situations. Although, it may be somewhat ambitious to expect model data capture all the essential features of these objects for target characterization, it will provide us with clues on how to augment the training datasets using perhaps a limited training samples from sonar returns of actual objects.
- Target Direction, Range and Position Finding