RFID Localization System Based on K-Nearest Neighbor Algorithm and Extreme Learning Machine Algorithm with Virtual Reference Tags

Changsheng Zhang, Yaoxian Li

Abstract


Aiming at the complex indoor environment, a new indoor localization algorithm was proposed based on the K-nearest neighbour algorithm (KNN) and extreme learning machine (ELM) by using virtual reference tags. In this paper, LANDMARC location algorithm is used in regional location during the online phase, and the ELM with virtual reference tags was introduced in the locked area. The design scheme of indoor positioning system based on Intel R1000 platform is proposed, and the system was realized by C++ language. The experimental data show that average error of the indoor positioning system is 0.3m and the reduction in estimation error is 38% over LANDMARC and 19% over ELM. The system can effectively improve the indoor localization accuracy in low tag density environments.

Keywords


Radio frequency identification(RFID); localization system; landmark; extreme learning machine; virtual reference.

Full Text:

PDF

References


Huang, Chih-Ning., and Chan,Chia-Tai., “ZigBee-based indoor location system by k-nearest neighbor algorithm with weighted RSSI” [J]. Procedia Computer Science, 2011, pp. 58-65.

Bahl, P., and Padmanabhan, V., “Radar: An in-building RF based user location and tracking system”, in proceedings of IEEE Infocom, 2000, pp. 775-784.

Xiao,W., Ni, W., and Toh, Y., “Integrated Wi-Fi Fingerprinting and Inertial Sensing for Indoor Positioning”. International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2011, pp.1-6.

Meng, W., Xiao, W. Ni, W., and Xie, L., “Secure and Robust Wi-Fi Fingerprinting Indoor Localization”, 2011 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2011, pp.1-7.

Huang, G.B., Zhu, Q.Y, and Siew, C.K., “Extreme Learning Machine: a New Learning Scheme of Feed forward Neural Networks” [C]. Proceedings of the International Joint Conference on Neural Networks. Piscataway: Institute of Electrical and Electronics Engineers Inc, 2004, pp.985-990.

Zou, H., Wang, H., Xie, L., and Jia, Q.S., “An RFID indoor positioning system by using weighted path loss and extreme learning machine”, in Cyber-Physical Systems, Networks, and Applications (CPSNA),IEEE 1st International Conference on,2013, pp. 66-71.

Zhao, Y.Z., Liu, Y.H., Lionel, M., and Ni, L.M., “Vire: active RFID-based localization using virtual reference elimination” [C].The 2007 international Conference on Parallel Processing (ICPC 2007), 2007, pp. 56-56.

Li, J.H., Qi, R., Wang, Y., and Wang, F., “An RFID Location Model Based On Virtual Reference Tag Space”, Journal of Computational Information Systems, June 2011, pp. 2104-2111.

Ni, L., Liu, Y., Lau, Y., and Patil, A., “LANDMARC: indoor location sensing using active RFID” [A]. Proceedings of the First IEEE International Conference on Pervasive Computing and Communications (PerCom2003) [C]. Dallas, Texas, USA, March 2003, pp. 407-415

Bahl, P., Padmanabhan, V.N., and Balachandran, A., “Enhancements to the RADAR User Location and Tracking System” [R]. Microsoft Research Technical Report, 2009.

Sun, Y., Fan, Z.P., “RFID technology and its application in indoor positioning” [J]. Journal of Computer Applications, 2005, (5), pp. 1205-1208.

Ni, L., Liu, Y., Lau, Y., and Patil, A., “LANDMARC: Indoor Location Sensing Using Active RFID”, Wireless networks, vol. 10, no. 6, 2004, pp. 701–710.

Liu, W., “Matlab and C/C++ mixed program design”, Beijing, Beihang University Press, 2005, pp.56-59.

Ruan, S.Y., “MATLAB Program design”, Beijing, Publishing House of Electronics Industry, 2004, pp.65-6


Refbacks

  • There are currently no refbacks.


 
 
  
 

 

  


About ASRJETS | Privacy PolicyTerms & Conditions | Contact Us | DisclaimerFAQs 

ASRJETS is published by (GSSRR).