Radar target classification using improved Dempster–Shafer theory

This study considers the problem of coarse classification of targets using multifunction radar. Several methods are available for classification such as decision trees, Dempster–Shafer, Bayes, neural networks, etc. A different approach to assign the mass functions based on fuzzy logic in the Dempste...

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Bibliographic Details
Published in:Journal of engineering (Stevenage, England) England), 2019-11, Vol.2019 (21), p.7872-7875
Main Authors: Mehta, Parth, De, Anindita, Shashikiran, Dayalan, Ray, Kamla Prasan
Format: Article
Language:English
Subjects:
coarse classification
Dempster–Shafer theory
fuzzy logic
IET International Radar Conference (IRC 2018)
inference mechanisms
mass functions
multifunction radar
radar target classification
radar target recognition
real-time systems
uncertainty handling
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Summary:This study considers the problem of coarse classification of targets using multifunction radar. Several methods are available for classification such as decision trees, Dempster–Shafer, Bayes, neural networks, etc. A different approach to assign the mass functions based on fuzzy logic in the Dempster–Shafer framework is proposed in this study. The method is evaluated for classification of different kinds of targets like aircraft, ballistic missiles, satellites, chaff and actual clouds, and unknown targets. With the proposed method, improvement in classification accuracy is observed, compared to existing mass functions. The technique is found to be computationally efficient and suitable for real-time systems.
ISSN:2051-3305
2051-3305
DOI:10.1049/joe.2019.0676