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Proceedings Paper

Fuzzy logic based sensor performance evaluation of vehicle mounted metal detector systems
Author(s): Canicious Abeynayake; Minh Dao-Johnson Tran
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Paper Abstract

Vehicle Mounted Metal Detector (VMMD) systems are widely used for detection of threat objects in humanitarian demining and military route clearance scenarios. Due to the diverse nature of such operational conditions, operational use of VMMD without a proper understanding of its capability boundaries may lead to heavy causalities. Multi-criteria fitness evaluations are crucial for determining capability boundaries of any sensor-based demining equipment. Evaluation of sensor based military equipment is a multi-disciplinary topic combining the efforts of researchers, operators, managers and commanders having different professional backgrounds and knowledge profiles. Information acquired through field tests usually involves uncertainty, vagueness and imprecision due to variations in test and evaluation conditions during a single test or series of tests. This report presents a fuzzy logic based methodology for experimental data analysis and performance evaluation of VMMD. This data evaluation methodology has been developed to evaluate sensor performance by consolidating expert knowledge with experimental data. A case study is presented by implementing the proposed data analysis framework in a VMMD evaluation scenario. The results of this analysis confirm accuracy, practicability and reliability of the fuzzy logic based sensor performance evaluation framework.

Paper Details

Date Published: 14 May 2015
PDF: 14 pages
Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 94540A (14 May 2015); doi: 10.1117/12.2087299
Show Author Affiliations
Canicious Abeynayake, Defence Science and Technology Organisation (Australia)
Minh Dao-Johnson Tran, Univ. of South Australia (Australia)

Published in SPIE Proceedings Vol. 9454:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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