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Electronics 2018, 7(2), 19; https://doi.org/10.3390/electronics7020019

RFID 3D-LANDMARC Localization Algorithm Based on Quantum Particle Swarm Optimization

School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China
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Received: 14 January 2018 / Revised: 3 February 2018 / Accepted: 6 February 2018 / Published: 9 February 2018
(This article belongs to the Special Issue RFID, WPT and Energy Harvesting)
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Abstract

Location information is crucial in various location-based applications, the nodes in location system are often deployed in the 3D scenario in particle, so that localization algorithms in a three-dimensional space are necessary. The existing RFID three-dimensional (3D) localization technology based on the LANDMARC localization algorithm is widely used because of its low complexity, but its localization accuracy is low. In this paper, we proposed an improved 3D LANDMARC indoor localization algorithm to increase the localization accuracy. Firstly, we use the advantages of the RBF neural network in data fitting to pre-process the acquired signal and study the wireless signal transmission loss model to improve localization accuracy of the LANDMARC algorithm. With the purpose of solving the adaptive problem in the LANDMARC localization algorithm, we introduce the quantum particle swarm optimization (QPSO) algorithm, which has the technology advantages of global search and optimization, to solve the localization model. Experimental results have shown that the proposed algorithm improves the localization accuracy and adaptability significantly, compared with the basic LANDMARC algorithm and particle swarm optimization LANDMARC algorithm, and it can overcome the shortcoming of slow convergence existed in particle swarm optimization. View Full-Text
Keywords: three-dimensional localization algorithm; LANDMARC; RBF neural network; QPSO three-dimensional localization algorithm; LANDMARC; RBF neural network; QPSO
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Wu, X.; Deng, F.; Chen, Z. RFID 3D-LANDMARC Localization Algorithm Based on Quantum Particle Swarm Optimization. Electronics 2018, 7, 19.

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