Next Article in Journal
Neural Network Based Maximum Power Point Tracking Control with Quadratic Boost Converter for PMSG—Wind Energy Conversion System
Next Article in Special Issue
Practical Energy Harvesting for Batteryless Ambient Backscatter Sensors
Previous Article in Journal
Digital Control Techniques Based on Voltage Source Inverters in Renewable Energy Applications: A Review
Previous Article in Special Issue
Proactive Redundant Data Filtering Scheme for Combined RFID and Sensor Networks
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle

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

School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China
Authors to whom correspondence should be addressed.
Electronics 2018, 7(2), 19;
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)
PDF [1546 KB, uploaded 9 February 2018]


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Wu, X.; Deng, F.; Chen, Z. RFID 3D-LANDMARC Localization Algorithm Based on Quantum Particle Swarm Optimization. Electronics 2018, 7, 19.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Electronics EISSN 2079-9292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top