Low complexity Wireless Indoor Positioning approaches based on fingerprinting techniques

Among different Wireless Indoor Positioning Systems (WIPS) that utilize radio frequency signal to estimation an object location, the system that uses Received Signal Strength (RSS) of the wireless transceiver, such as the fingerprinting approach, has gained lot of attention and been deployed widely. This paper presents a novel wireless indoor positioning approaches based on fingerprinting technique called Fusion parameter technique with temperature level (FUSTL). The proposed technique takes into account an environmental data in conjunction with RSS information in the fingerprinting database. The environmental data, such as room temperature and humidity, is used to classify different zones of the service areas. This information could help facilitate the RSS matching process of the fingerprinting technique. The performance of the proposed algorithm is compared with those of different indoor positioning techniques in literature. The experimental results shows that the proposed technique outperformed the other techniques and can provide the highest of accuracy and precision performance at the lowest computational complexity. Specifically, the positioning accuracy of our proposed technique is 15-27% higher than those of the other techniques whereas the computational complexity of our technique is 12-36% lower than those of the other techniques.