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

Shi, Weiguang1,2; Du, Jiangxia1,2; Cao, Xiaowei1,2; Yu, Yang3; Cao, Yu4; Yan, Shuxia1,2; Ni, Chunya5

2019-02-02

10.3390/s19040968

SENSORS   影响因子和分区

1424-8220

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.

UHF RFID directional radiation indoor localization kNN

SCI ; EI

英语

其他

Scientific Research Project of Tianjin Education Commission[2017KJ088]

Chemistry ; Electrochemistry ; Instruments & Instrumentation

Chemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation

WOS:000460829200219

MDPI

20191306706668

Dipole antennas ; Indoor positioning systems ; Learning algorithms ; Microstrip antennas ; Nearest neighbor search ; Radio frequency identification (RFID) ; Slot antennas

Radio Systems and Equipment:716.3 ; Optimization Techniques:921.5

CHEMISTRY

Web of Science

1.Tianjin Polytech Univ, Sch Elect & Informat Engn, Tianjin 300387, Peoples R China
2.Tianjin Polytech Univ, Tianjin Key Lab Optoelect Detect Technol & Syst, Tianjin 300387, Peoples R China
3.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen 518055, Peoples R China
4.Tianjin Univ, Sch Marine Sci & Technol, Tianjin 300072, Peoples R China
5.China Mobile Commun Grp Tianjin Co Ltd, Tianjin 300308, Peoples R China

Shi, Weiguang,Du, Jiangxia,Cao, Xiaowei,et al. IKULDAS: An Improved kNN-Based UHF RFID Indoor Localization Algorithm for Directional Radiation Scenario[J]. SENSORS,2019,19(4).

Shi, Weiguang.,Du, Jiangxia.,Cao, Xiaowei.,Yu, Yang.,Cao, Yu.,...&Ni, Chunya.(2019).IKULDAS: An Improved kNN-Based UHF RFID Indoor Localization Algorithm for Directional Radiation Scenario.SENSORS,19(4).

Shi, Weiguang,et al."IKULDAS: An Improved kNN-Based UHF RFID Indoor Localization Algorithm for Directional Radiation Scenario".SENSORS 19.4(2019).