In traditional VIRE method, the linear interpolation is used to estimate the relationship between RSSI and distance, this increases the positioning error. Moreover, the label elimination threshold is a fixed value that is obtained by repeated adjustment experiments, this increases the time complexity of positioning. For these two problems, an adaptive VIRE indoor positioning algorithm is proposed based on Kriging interpolation. The Kriging interpolation is used to estimate the RSSI value of the virtual tag, and the threshold is automatically adjusted according to the real-time characteristics of the pending tag, so that the number of adjacent tags is nearly optimal, and the interference is more accurately eliminated, thereby the positioning coordinates are obtained. The experimental comparison results show that the algorithm not only saves the positioning cost, but also improves the positioning accuracy.
Ahn,H.S., Yu,W. (2009). Environmental-adaptive RSSI-based indoor localization, IEEE Transactions on Automation Science and Engineering, 6(4), 626–633.
2.
Chen,Z.P. (2019). An RFID anti-collision Q-value algorithm research, International Journal of RF Technologies, 10(1-2), 27–37. DOI: 10.3233/RFT-180146
3.
Digiampaolo,E., Martinelli,F. (2012). A Passive UHF-RFID System for the Localization of an Indoor Autonomous Vehicle, IEEE Transactions on Industrial Electronics, 10(10), 3961–3970.
4.
Huang,C.H., et al. (2015). Real-Time RFID Indoor Positioning System Based on Kalman-Filter Drift Removal and Heron-Bilateration Location Estimation, IEEE Transactions on Instrumentation and Measurement, 3(3), 728–739.
5.
Jiang,X., Liu,Y., Wang,X. (2009). An enhanced approach of indoor location sensing using active RFID, in WASE International Conference on Information Engineering, 1, 169–172.
6.
Jiang,L.F., Lu,G.Z., Xin,Y.W. (2007). Research on Anti-collision Algorithm in Radio Frequency Identification System, Computer Engineering and Applications, 43(15), 29–32. DOI: 10.3321/j.issn:1002-8331.2007.15.009
7.
Le,N.D., Zidek,J.V., et al. (2006). Statistical analysis of environmental space-time processes. Springer Science & Business Media, pp. 101–134.
8.
Liu,H.Y., Ma,J.H., et al. (2017). Construction method of fingerprint database based on improved Kriging interpolation for indoor location, Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 29(6), 751–757. DOI: 10.3979/j.issn.1673-825X.2017.06.007
9.
Li,N., Becerik-Gerber,B. (2011). Performance-based evaluation of RFID-based indoor location sensing solutions for the built environment, Advanced Engineering Informatics, 25(3), 535–546.
10.
Li,F., Wang,T.C., Pei,W.M. (2012). Construction method of electronic map based on spatial variability theory, Journal of Harbin Engineering University, 6, 715–719. DOI: 10.3969/j.issn.1006-7043.201108039
11.
Li,Q.Y., Wang,N.C., Yi,D.Y. (2010). Numerical Analysis. 5 edition. Beijing: Tsinghua University Press, pp. 23–35.
12.
Lu,Y.M., Wang,L., et al. (2018). Kriging interpolation method based on semi-supervised learning, Computer Engineering and Applications, 54(22), 265–270. DOI: 10.3778/j.issn.1002-8331.1708-0031
13.
Ni,L.M., Liu,Y., Lau,Y.C., et al. (2004). LANDMARC: Indoor location sensing using active RFID, Wireless Networks, 10(6), 701–710.
14.
Park,S., Lee,H. (2013). Self-Recognition of Vehicle Position Using UHF Passive RFID Tags, IEEE Transactions on Industrial Electronics, 1(1), 226–234.
15.
Peng,J.S., Qin,Y., Yong,et al. (2019). RFID-based indoor mobile robot navigation, International Journal of RF Technologies, 10(1-2), 1–8. DOI: 10.3233/RFT-190176
16.
Rasmussen,C.E., Williams,C.K.I., et al. (2006), Gaussian Processes for Machine Learning, MIT Press, pp. 7–30.
17.
Saad,S.S., Nakad,Z.S., et al. (2011). A Standalone RFID Indoor Positioning System Using Passive Tags, IEEE Transactions on Industrial Electronics, 5(5), 1961–1970.
18.
Shang,H. (2018). A lightweight RFID tags anti-collision scheme based on searching tree, Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 30(6), 789–796. DOI: 10.3979/j.issn.1673-825X.2018.06.009
19.
Wang,Y.Z., Mao,L.H., Liu,H., et al. (2010). Research and application of radio frequency identification and location algorithm based on reference label, Journal on Communications, 31(2), 86–92. DOI: 10.3969/j.issn.1000-436X.2010.02.013
20.
Wu,F., Tan,X.B. (2017). RFID Indoor Positioning Algorithm Based on Antenna Coverage Model, Computer Engineering, 43(3), 120–125. DOI: 10.3969/j.issn.1000-3428.2017.03.022
21.
Xu,Y.J., Wang,Y., Yan,D.H., et al. (2017). RFID positioning algorithm based on Newton interpolation and hybrid grey wolf optimization SVR, Journal of System Simulation, 29(9), 1921–192. DOI: 10.16182/j.issn1004731x.joss.201709007
22.
Xu,A.P., Hu,L., Shu,H. (2011). Space-time extension and implementation of space Kriging interpolation, Journal of Computer Applications, 31(1), 273–276. DOI: 10.3724/SP.J.1087.2011.00273
23.
Yin,S., Chen,Y.Y., et al. (2018). Indoor moving-target localization using RFID and adaptive Kalman filter, Journal of Nanjing University of Information Science & Technology, 10(6), 749–753. DOI: 10.16182/j.issn1004731x.joss.201709007
24.
Zhai,W. (2018). A new positioning system based on radio frequency identification and neighbourhood positioning algorithm, International Journal of RF Technologies, 9(3-4), 89–99. DOI: 10.3233/RFT-180120
25.
Zhang,Y.J., Chen,E.X. (2015). An approximate sphere of the four anchor nodes positioning method based on RSSI, Sensors & Transducers, 186(3), 85–92.
26.
Zhao,Y., Hong,W., Cheung,S.C. (2010). The impact of reader to tag collision on RFID tag identification, in International Conference on Wireless Algorithms, Systems, and Applications.. Berlin, Germany: Springer, pp. 115–119.
27.
Zhao,Y., Liu,Y., Ni,L.M. (2007). VIRE: Active RFID-based localization using virtual reference elimination, in International Conference on Parallel Processing, pp. 56–66.
