Abstract
A cluster-based intersection fingerprinting technique for outdoor location estimation using received signal strength (RSS) is proposed. The performance of the proposed scheme is demonstrated by making comparisons using RSS data from a simulated grid-based urban propagation model, RSS data generated by a network planning tool applied to a rural environment, and RSS data from real city environment. The proposed scheme first uses an optimal clustering scheme to portion the large outside area into different small regions based on the use of RSS deviations from the path loss model. For each region, a fine RSS distribution model is built to provide a good support for further positioning. An improved intersection method is then presented to find the most likely geographical area to further estimate a mobile user's location. A comparison between cluster-based and grid-based environment partitioning is made. The experimental results show that the proposed clustering scheme gives good support for location estimation and the positioning accuracy is significantly improved.
Highlights
Localisation has become more and more popular in pervasive computing environments, for example, positioning a mobile user in an emergency environment
The performance of the proposed scheme is demonstrated by making comparisons using Received Signal Strength (RSS) data from a simulated grid-based urban propagation model, RSS data generated by a network planning tool applied to a rural environment, and RSS data from real city environment
We propose a outdoor location estimation scheme exhibiting a high accuracy in localising mobile stations (MSs) even with a relatively low density of reference RSS data
Summary
Localisation has become more and more popular in pervasive computing environments, for example, positioning a mobile user in an emergency environment. A variety of wireless localisation techniques have been proposed in the literature, including Time of Arrival (ToA), Time Difference of Arrival (TDoA), Angle of Arrival (AoA) and Received Signal Strength (RSS)-based methods. We propose a outdoor location estimation scheme exhibiting a high accuracy in localising mobile stations (MSs) even with a relatively low density of reference RSS data. In the first phase the outdoor area is partitioned into small clusters by analysing the RSS collected from historical data using an improved clustering scheme. In the online localisation phase the mobile location can be estimated by further analysing these clusters with the help of a refined intersection approach.
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