Underwater Mine Detection Using Acoustic Gap Filler and Electro-optic Sensor Data

Feature-based target detection and identification data fusion algorithms have been shown to be effective in classifying land mines and other objects. Several of these algorithms, namely Dempster-Shafer inference, voting logic fusion, and Bayesianinference, may be particularly applicable to the detection of underwater mines. WaveBand will utilize its extensive experience in applying these techniques for selecting and/or developing data fusion methods that are optimal for the underwater minedetection problem using acoustic gap filler and electro-optic sensor data. Our ability to understand the strengths and limitations of data fusion algorithms and the information required as inputs to the algorithms makes WaveBand the ideal candidate toaddress this challenge for the Navy. The algorithms cited require expert knowledge or information that is frequently obtained from the results of