Use of RSSI and time-of-flight wireless signal characteristics for location tracking

Authors:
Joshua J. Davies;Farhad Kamangar;Gergely Zaruba;Manfred Huber;Vassilis Athitsos
Affiliations:
Travelocity, Southlake, TX;University of Texas at Arlington, Arlington, TX;University of Texas at Arlington, Arlington, TX;University of Texas at Arlington, Arlington, TX;University of Texas at Arlington, Arlington, TX
Venue:
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Year:
2011

Citing 6
Cited 0

Practical robust localization over large-scale 802.11 wireless networks

Proceedings of the 10th annual international conference on Mobile computing and networking
Robotics-based location sensing using wireless Ethernet

Wireless Networks
Indoor location tracking using RSSI readings from a single Wi-Fi access point

Wireless Networks
Comparison of the Mechanisms of the Zigbee's Indoor Localization Algorithm

SNPD '08 Proceedings of the 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
Achieving range-free localization beyond connectivity

Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Semi-definite programming algorithms for sensor network node localization with uncertainties in anchor positions and/or propagation speed

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Wireless networking is a common component in assistive environment designs. In addition to allowing the deployment of multiple sensors that are not physically connected, wireless networking carries an additional benefit - the characteristics and interactions between the wireless mobile node and the wireless base can be examined to determine the otherwise unknown location of the mobile node. Determining the location of the mobile node is the topic of this paper. In particular, this paper explores the relationship between Received Signal Strength Indicator (RSSI) and the error in the computed Time-of-Flight (TOF) data. This relationship is exploited to associate probabilities with location readings which are subsequently used to apply particle filtering techniques to reduce the inaccuracy in the location computation. We show that by using RSSI and particle filtering, the wireless node's location can be tracked far more accurately than by accepting TOF as provided.