LANDMARC: Indoor Location Sensing Using Active RFID
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Distributed localization in wireless sensor networks: a quantitative comparison
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks
A kernel-based learning approach to ad hoc sensor network localization
ACM Transactions on Sensor Networks (TOSN)
What does RFID do for the consumer?
Communications of the ACM - Special issue: RFID
Mobile RFID — A Case from Volvo on Innovation in SCM
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
Statistical learning theory for location fingerprinting in wireless LANs
Computer Networks and ISDN Systems
A proximity-based method for locating RFID tagged objects
Advanced Engineering Informatics
Accurate and low-cost location estimation using kernels
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
Error analysis of non-collaborative wireless localization in circular-shaped regions
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Localization of tagged objects is a valuable addition to the application of radio frequency identification (RFID) technology. This paper studies the localization performance in an indoor environment with an active RFID system. Three major localization methods, namely, multi-lateration, nearest-neighbor, and support vector machines (SVM) methods are implemented, and their results are compared. It shows that the SVM method outperforms the other two in terms of localization accuracy, but the three methods produce comparable localization precision values. For each method, observations and discussion are provided on how data sample size, beacon number, or neighbor number influences localization performance. In addition, time complexities of the three methods are compared. It is pointed out that, for practical applications, a balanced consideration of localization performance, method, hardware cost, and data collection effort is required for economical and yet satisfactory solutions.