Passive Localization Algorithm for Remote Multitarget Localization Information

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‐propagati...

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Vydané v:IEEJ transactions on electrical and electronic engineering Ročník 15; číslo 8; s. 1183 - 1187
Hlavní autori: Niu, Gang, Gao, Jie, Du, Taihang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hoboken, USA John Wiley & Sons, Inc 01.08.2020
Wiley Subscription Services, Inc
Predmet:
Algorithms
Artificial neural networks
Computer simulation
Errors
Localization
Machine learning
Model accuracy
Multiple target tracking
neural network
Neural networks
Particle swarm optimization
passive localization
target location information field
Wireless communications
ISSN:1931-4973, 1931-4981
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1931-4973
1931-4981
DOI:10.1002/tee.23179