LANDMARC: indoor location sensing using active RFID
Wireless Networks - Special issue: Pervasive computing and communications
The Horus WLAN location determination system
Proceedings of the 3rd international conference on Mobile systems, applications, and services
Location-based Services: Fundamentals and Operation
Location-based Services: Fundamentals and Operation
Multipath profile discrimination in TOA-based WLAN ranging with link layer frames
WiNTECH '06 Proceedings of the 1st international workshop on Wireless network testbeds, experimental evaluation & characterization
Statistical learning theory for location fingerprinting in wireless LANs
Computer Networks and ISDN Systems
Practical network-based techniques for mobile positioning in UMTS
EURASIP Journal on Applied Signal Processing
Extensible platform for location based services provisioning
WISEW'03 Proceedings of the Fourth international conference on Web information systems engineering workshops
A comprehensive multi-factor analysis on RFID localization capability
Advanced Engineering Informatics
Experimental comparison of bluetooth and wifi signal propagation for indoor localisation
WWIC'12 Proceedings of the 10th international conference on Wired/Wireless Internet Communication
Implement the RFID Position Based System of Automatic Tablets Packaging Machine for Patient Safety
Journal of Medical Systems
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This paper presents a positioning algorithm, named time of arrival to time difference of arrival (TOAD), which computes time-difference-of-arrival (TDOA) measurements from the messages that time-of-arrival (TOA) stations in sight exchange while their positioning processes are running. This study addresses the accuracy of the TOAD algorithm in two different environments: line-of-sight (LOS) and non-line-of-sight (NLOS). Simulation is used to set up a wireless network. The Gauss-Newton nonlinear least squares algorithm is used to compute the positions in both TOA and TOAD stations. Results indicate that the TOAD algorithm increases the root mean square error (RMSE) of the positioning error in LOS scenarios by 10 to 20% compared with the RMSE achieved by TOA. This drop in accuracy contrasts with the results for the NLOS scenarios. The RMSE of TOAD in such scenarios is at least 10% lower than that achieved by TOA. This result is specially important since this latter scenario is the most common. Consequently, this novel technique therefore improves the scalability and integrity of TOA techniques based on RTT, making it possible for the stations to position themselves without injecting traffic and with QoS figures close and most times better than that achieved by TOA.