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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| Published in: | IEEJ transactions on electrical and electronic engineering Vol. 15; no. 8; pp. 1183 - 1187 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Hoboken, USA
John Wiley & Sons, Inc
01.08.2020
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| ISSN: | 1931-4973, 1931-4981 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Gao, Jie Niu, Gang Du, Taihang |
| Author_xml | – sequence: 1 givenname: Gang surname: Niu fullname: Niu, Gang email: n_gang@yeah.net organization: Institute of Mechanical Technology – sequence: 2 givenname: Jie surname: Gao fullname: Gao, Jie organization: Hebei University of Technology – sequence: 3 givenname: Taihang surname: Du fullname: Du, Taihang organization: Hebei University of Technology |
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| Copyright | 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. |
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| SubjectTerms | 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 |
| Title | Passive Localization Algorithm for Remote Multitarget Localization Information |
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