Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 6, Issue 2


Published
on

March 31, 2022


Pages

713-720


DOI

Article

TOPSIS missile target selection method supported by the posterior probability of target recognition

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Authors

Zhang Jianqiang Affiliation:
Institute of Ordnance Engineering, Naval University of Engineering, Wuhan 430033, China
, Li Jialin Affiliation:
Institute of Ordnance Engineering, Naval University of Engineering, Wuhan 430033, China
and Li Yan Affiliation:
Institute of Ordnance Engineering, Naval University of Engineering, Wuhan 430033, China


Abstract

Addressing the problem of being easily interfered by false targets such as chaff and corner reflector when selecting missile targets, a multi-feature multi-model target selection method based on the technique for order preference by similarity to ideal solution (TOPSIS) is proposed if only single-target characteristics such as target position or radar cross section (RCS) size is used in the selection of missile targets. The target selection problem under the comprehensive utilisation of multiple features is regarded as a problem in decision-making based on multiple attributes. The BP network based on the target RCS frequency domain statistical features, the radial basis network based on the target polarisation feature and the radial size feature based on high-resolution range profile (HRRP) are realised. The comprehensive utilisation of C-support vector machine (SVM) and other target recognition methods and the comprehensive sorting of target selection, to a certain extent, enable anti-ship missiles to make the correct target selection more accurately. The simulation results display that the effective selection of ship targets can be achieved in the case of passive interference by surface ships.


Keywords

vector machines, neural networks, TOPSIS, target recognition


Citation

Jianqiang, Z., Jialin, L., & Yan, L. (2021). TOPSIS missile target selection method supported by the posterior probability of target recognition. Applied Mathematics and Nonlinear Sciences, 6(2), 713–720. https://doi.org/10.2478/amns.2021.2.00160

Published by: Engineering Journals

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