WLAN-based Outdoor Localisation Using Pattern Matching Algorithm

Location detection is a very important issue in a wireless environment, allowing realisation of several useful services linked to the knowledge of the position of a mobile user (e.g. path optimisation, management of field crew in a large plant, providing a mobile robot with autonomous capabilities). Literature presents many approaches for location detection of a mobile device in a WLAN. One of these is based on the use of a pattern matching algorithm which provides for the position of the mobile device given the Radio Signal Strength (RSS) values received by it. Generally, localisation in WLAN environment is limited to indoor scenarios. Although GPS is commonly used for outdoor location detection, localisation in a WLAN communication infrastructure may be realised also for outdoor areas. The main advantage is a save in the hardware needed for the localisation, as it only requires the use of the WLAN communication card; further, the current limits of GPS, which may introduce not negligible errors in outdoor location detection, encourages the investigation of other techniques for outdoor localisation. On account of what said, the first aim of the paper is to investigate the feasibility of outdoor localisation of a mobile device in a WLAN environment; an IEEE 802.11b-based WLAN will be considered. Outdoor localisation has been realised using a pattern matching algorithm and the relevant performance, measured in terms of location errors, has been evaluated and compared with that commonly provided for by a GPS device. Then the paper will present a proposal for localisation, aimed to reduce the complexity typically featured by the pattern matching algorithm based approach when used for wide areas; the proposal is based on the use of a signal propagation model able to predict the RSS values available in each position of a IEEE 802.11b WLAN. Performance of the novel approach has been evaluated for outdoor localisation and the main results achieved will be shown in the paper. Finally, use of a Kalman filter for the location detection, will be presented in order to improve the performance of the pattern matching algorithm, making this technique very attractive for outdoor use.