Outdoor Location Estimation in Changeable Environments

• Published in: IEEE communications letters Vol. 17; no. 11; pp. 2072 - 2075
• Main Authors: Kejiong Li, Bigham, John, Bodanese, Eliane L., Tokarchuk, Laurissa
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.2013; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Calibration; Detection, estimation, filtering, equalization, prediction; Equipments and installations; Estimation; Exact sciences and technology; Global Positioning System; Information, signal and communications theory; Location estimation; Meteorology; Mobile radiocommunication systems; radio map; Radiocommunications; received signal strength; Signal and communications theory; Signal, noise; Sociology; Telecommunications; Telecommunications and information theory; Training; Training data; Performance evaluation; Mobile radiocommunication; Parameter estimation; State of the art; GSM system; radio map; Wireless telecommunication; Outdoor installation; Calibration; Location estimation; Humidity; received signal strength; Localization; Deterministic algorithms
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2013.092813.131427

One approach to location estimation constructs a radio map of received signal strength (RSS) measurements at different known locations. However, location-based systems that depend on RSS alone are susceptible to inaccuracies caused by several factors, such as changes in humidity, temperature, the number of users and the physical environment. In this paper, we present a novel algorithm in which one radio map is generated as a reference map and adjusted for different run-time environmental conditions. A small sample of new RSS samples for the new environment is collected, with locations, and used to build a model to calibrate new measurements to the reference radio map. The calibration is not uniform and depends on the observed RSS. The effectiveness of the proposed method is demonstrated using real GSM data sets collected from a three-day music festival in London Victoria Park. Results are presented with and without applying the correction. A state-of-the-art cluster-based deterministic location estimation algorithm is used throughout.