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Passive Localization Algorithm for Remote Multitarget Localization Information
Author(s) -
Niu Gang,
Gao Jie,
Du Taihang
Publication year - 2020
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.23179
Subject(s) - particle swarm optimization , extreme learning machine , algorithm , computer science , backpropagation , artificial neural network , wireless , artificial intelligence , telecommunications
The development of wireless communication has led to the wide application of passive localization. Although passive localization has strong antijamming and concealment, its accuracy will be reduced due to false location. In order to solve the above problem, particle swarm optimization–back‐propagation (PSO‐BP) algorithm was used to improve the accuracy of passive location model in this study, and it was compared with the traditional BP algorithm and extreme learning machine (ELM) algorithm by simulation. The results showed that the coordinate error calculated by the PSO‐BP neural network was smaller than that of the BP neural network and ELM algorithms, and the error fluctuation was smaller; with the increase of the number of multitarget localization, the average error and positioning time of the BP algorithm gradually increased, while the average positioning error and positioning time of the PSO‐BP algorithm basically remained stable, smaller than the BP algorithm. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.