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IKULDAS: An Improved kNN-Based UHF RFID Indoor Localization Algorithm for Directional Radiation Scenario

1
School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China
2
Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin Polytechnic University, Tianjin 300387, China
3
Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China
4
School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
5
China Mobile Communication Group Tianjin Co., Ltd., Tianjin 300308, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(4), 968; https://doi.org/10.3390/s19040968
Received: 29 January 2019 / Revised: 16 February 2019 / Accepted: 18 February 2019 / Published: 25 February 2019
(This article belongs to the Special Issue Augmented RFID Technologies for the Internet of Things and Beyond)
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Abstract

Ultra high frequency radio frequency identification (UHF RFID)-based indoor localization technology has been a competitive candidate for context-awareness services. Previous works mainly utilize a simplified Friis transmission equation for simulating/rectifying received signal strength indicator (RSSI) values, in which the directional radiation of tag antenna and reader antenna was not fully considered, leading to unfavorable performance degradation. Moreover, a k-nearest neighbor (kNN) algorithm is widely used in existing systems, whereas the selection of an appropriate k value remains a critical issue. To solve such problems, this paper presents an improved kNN-based indoor localization algorithm for a directional radiation scenario, IKULDAS. Based on the gain features of dipole antenna and patch antenna, a novel RSSI estimation model is first established. By introducing the inclination angle and rotation angle to characterize the antenna postures, the gains of tag antenna and reader antenna referring to direct path and reflection paths are re-expressed. Then, three strategies are proposed and embedded into typical kNN for improving the localization performance. In IKULDAS, the optimal single fixed rotation angle is introduced for filtering a superior measurement and an NJW-based algorithm is advised for extracting nearest-neighbor reference tags. Furthermore, a dynamic mapping mechanism is proposed to accelerate the tracking process. Simulation results show that IKULDAS achieves a higher positioning accuracy and lower time consumption compared to other typical algorithms. View Full-Text
Keywords: UHF RFID; directional radiation; indoor localization; kNN UHF RFID; directional radiation; indoor localization; kNN
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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).
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Shi, W.; Du, J.; Cao, X.; Yu, Y.; Cao, Y.; Yan, S.; Ni, C. IKULDAS: An Improved kNN-Based UHF RFID Indoor Localization Algorithm for Directional Radiation Scenario. Sensors 2019, 19, 968.

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