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Automatika
Journal for Control, Measurement, Electronics, Computing and Communications
Volume 60, 2019 - Issue 4
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Regular Papers

Monte Carlo localization algorithm based on particle swarm optimization

Cuiran LiSchool of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, People's Republic of ChinaView further author information
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Jianli XieSchool of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, People's Republic of ChinaCorrespondence[email protected]
View further author information
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Wei WuSchool of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, People's Republic of ChinaView further author information
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Haoshan TianSchool of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, People's Republic of ChinaView further author information
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Yingxin LiangSchool of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, People's Republic of ChinaView further author information
Pages 451-461 | Received 02 Jan 2018, Accepted 30 Jun 2019, Published online: 28 Jul 2019

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Xumei Lin, Guanghui Zhu, Shijie Yu, Chuan Hu & Penggang Wang. (2023) Prediction of corrosion degree of reinforced concrete based on improved QPSO-neural network. Nondestructive Testing and Evaluation 38:3, pages 412-430.
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Ozan Vahit Altınpınar & Volkan Sezer. (2023) A novel indoor localization algorithm based on a modified EKF using virtual dynamic point landmarks for 2D grid maps. Robotics and Autonomous Systems 170, pages 104546.
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Elie Tagne Fute, Doris-Khöler Nyabeye Pangop & Emmanuel Tonye. (2022) A new hybrid localization approach in wireless sensor networks based on particle swarm optimization and tabu search. Applied Intelligence 53:7, pages 7546-7561.
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Jun Yang & Wu Lv. (2021) Optimization of Sports Training Systems Based on Wireless Sensor Networks Algorithms. IEEE Sensors Journal 21:22, pages 25075-25082.
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Baofeng Duan, Cuiran Li, Jianli Xie, Dongmei Zhou & Wei Wu. (2020) Positioning Algorithm Based on the Fingerprint Database by Twice-Fuzzy Clustering in the High-Speed Railway Scenario. IEEE Access 8, pages 64846-64856.
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