Research On Indoor Positioning Based On WLAN

With the development of indoor positioning, the techniques of RFID and WLAN get more and more attention from researchers. Because positioning method with WLAN needs no extra tools to locate, and the hotpots of WLAN are a prerequisite in indoor space, this technique became more and more important. However, due to the multipath effect and effect of NLOS, signal strength becomes unstable,and it deceases unstably. In this thesis, we have did some experiments and analyze on the result of these experiments.The positioning algorithm based on wireless signal is widely used in the indoor localization. However, the multipath effect and movements of people in indoor environments lead to inaccurate localization. Through the experiments, calculation and analysis on the RSSI(Received Signal Strength Indication) and the variance of RSSI, we propose a novel variance-based fingerprint distance adjustment algorithm(VFDA). Based on the rule that variance decreases with the increase of RSSI mean value, VFDA calculates RSSI variance with the mean value of received RSSIs. Then, we can get the corrected weight. VFDA adjusts the fingerprint distances with the correction weight based on the variance of RSSI, which is adopted to correct the fingerprint distance. Besides, a threshold value is applied to the VFDA to improve its performance. VFDA and VFDA with the threshold value are applied in the two real typical indoor environments deployed with several WIFI access points. One is a quadrate lab room, and the other is a long and narrow corridor of a building. Experimental results and performance analysis show that in both indoor environments, both VFDA and VFDA with the threshold VFDA has the better positioning accuracy and environmental adaptability than the current typical positioning methods based on the K-nearest neighbor algorithm and the weighted K-nearest neighbor algorithm with similar computational costs and the same hardware cost.Finally, in order to solve the problem of the blind points in large indoor areas, we mix up the WLAN APs in indoor areas and signals from the terminals with consideration of the WIFI tools in mobile phones. In this algorithm, we apply different calculating methods regarding to the specific cases of RSSI.