Acoustic backscatter classification for mine detection using multiple fused aspects and novel database classification rules

Christopher F. Barnes

doi:10.1117/12.324208

4 September 1998 Acoustic backscatter classification for mine detection using multiple fused aspects and novel database classification rules

Christopher F. Barnes

Author Affiliations +

Proceedings Volume 3392, Detection and Remediation Technologies for Mines and Minelike Targets III; (1998) https://doi.org/10.1117/12.324208
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

Undetected sea mines in a littoral environment are dangerous threats that must be first detected and then avoided or neutralized in the conduct of strategic and tactical warfare. The U.S. Navy is seeking enabling sensor suites and associated algorithms that allow autonomous underwater vehicles to search, detect and destroy sea mines. Acoustic backscatter is a sensing mechanism that permits searches to be conducted at comparatively long ranges and thus would enable high area coverage rates. The research problem addressed in this paper is the development of an algorithm that allows acoustic backscatter to be used to detect and classify mines and mine like objects (MLOs). This paper presents a novel approach of fusing and classifying multiple acoustic backscatter signals for the purpose of identifying mines and mine-like objects at long ranges. The algorithm relies on an underlying database of measured target signatures for classification purposes and uses a set of quick search templates that encapsulate the target information contained in this 'knowledge-pool' database. The templates are mathematically structured to permit database searches to be performed in real time with low to moderate computational resources. The mathematical structure of the search templates is hierarchical in nature and allows the signal processing tasks of mine detection, discrimination, and identification to be performed by a single integrated system in a progressive manner. This classification system also knows when data of an unknown nature is encountered.

Citation Download Citation

Christopher F. Barnes "Acoustic backscatter classification for mine detection using multiple fused aspects and novel database classification rules", Proc. SPIE 3392, Detection and Remediation Technologies for Mines and Minelike Targets III, (4 September 1998); https://doi.org/10.1117/12.324208

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