Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization
A TDOA/AOA hybrid location algorithm based on the crow search algorithm optimized by particle swarm optimization is proposed to address the challenge of solving the nonlinear equation of time of arrival (TDOA/AOA) location in the non-line-of-sight (NLoS) environment. This algorithm keeps its optimiz...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/2/186 |
| _version_ | 1797595911584481280 |
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| author | Li Cao Haishao Chen Yaodan Chen Yinggao Yue Xin Zhang |
| author_facet | Li Cao Haishao Chen Yaodan Chen Yinggao Yue Xin Zhang |
| author_sort | Li Cao |
| collection | DOAJ |
| description | A TDOA/AOA hybrid location algorithm based on the crow search algorithm optimized by particle swarm optimization is proposed to address the challenge of solving the nonlinear equation of time of arrival (TDOA/AOA) location in the non-line-of-sight (NLoS) environment. This algorithm keeps its optimization mechanism on the basis of enhancing the performance of the original algorithm. To obtain a better fitness value throughout the optimization process and increase the algorithm’s optimization accuracy, the fitness function based on maximum likelihood estimation is modified. In order to speed up algorithm convergence and decrease needless global search without compromising population diversity, an initial solution is simultaneously added to the starting population location. Simulation findings demonstrate that the suggested method outperforms the TDOA/AOA algorithm and other comparable algorithms, including Taylor, Chan, PSO, CPSO, and basic CSA algorithms. The approach performs well in terms of robustness, convergence speed, and node positioning accuracy. |
| first_indexed | 2024-03-11T02:44:12Z |
| format | Article |
| id | doaj.art-e0d020de4f2e43aea4f31f8f6fe8c090 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-11T02:44:12Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-e0d020de4f2e43aea4f31f8f6fe8c0902023-11-18T09:28:55ZengMDPI AGBiomimetics2313-76732023-04-018218610.3390/biomimetics8020186Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based LocalizationLi Cao0Haishao Chen1Yaodan Chen2Yinggao Yue3Xin Zhang4School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, ChinaSchool of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, ChinaSchool of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, ChinaSchool of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, ChinaSchool of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, ChinaA TDOA/AOA hybrid location algorithm based on the crow search algorithm optimized by particle swarm optimization is proposed to address the challenge of solving the nonlinear equation of time of arrival (TDOA/AOA) location in the non-line-of-sight (NLoS) environment. This algorithm keeps its optimization mechanism on the basis of enhancing the performance of the original algorithm. To obtain a better fitness value throughout the optimization process and increase the algorithm’s optimization accuracy, the fitness function based on maximum likelihood estimation is modified. In order to speed up algorithm convergence and decrease needless global search without compromising population diversity, an initial solution is simultaneously added to the starting population location. Simulation findings demonstrate that the suggested method outperforms the TDOA/AOA algorithm and other comparable algorithms, including Taylor, Chan, PSO, CPSO, and basic CSA algorithms. The approach performs well in terms of robustness, convergence speed, and node positioning accuracy.https://www.mdpi.com/2313-7673/8/2/186hybrid localizationmobile location estimationcrow search algorithmparticle swarm optimizationmaximum likelihood estimation |
| spellingShingle | Li Cao Haishao Chen Yaodan Chen Yinggao Yue Xin Zhang Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization Biomimetics hybrid localization mobile location estimation crow search algorithm particle swarm optimization maximum likelihood estimation |
| title | Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization |
| title_full | Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization |
| title_fullStr | Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization |
| title_full_unstemmed | Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization |
| title_short | Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization |
| title_sort | bio inspired swarm intelligence optimization algorithm aided hybrid tdoa aoa based localization |
| topic | hybrid localization mobile location estimation crow search algorithm particle swarm optimization maximum likelihood estimation |
| url | https://www.mdpi.com/2313-7673/8/2/186 |
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