Research On Key Technology Of WiFi - Based Wireless Location

With the development of society, the demand for indoor location service increases, Even if the technique of GPS can provide nice location server outside, but it cannot be used indoor, what is more, the weather and other external factors could affect it very easily. Therefore it needs to use other technical solutions to achieve indoor positioning. Through using the WiFi network for indoor positioning, can largely compensate for the limits of GPS and other outdoor positioning technology in the construction of concentrated area and indoor applications, and extended positioning service at the same time. In this paper, the study is based on the fingerprint location techniques which are based on WiFi network.The fingerprint location techniques which are based on WiFi network have many advantages such as high-speed location, minor error, good expansibility and many other virtues, it has turned into another location technique following GPS positioning, acoustic positioning and so on.Firstly this article has analyzed the domestic and foreign technology based on a variety of different technical solutions, then introduced the main factors of influencing the indoor positioning accuracy. Then through the introduction of the typical indoor propagation model, make a detailed analysis of the localization algorithm based propagation model.The third chapter of this paper introduces several typical fingerprint location algorithm, which analyzes the advantages and disadvantages of these algorithms, and aimed at these shortcomings, innovative method is proposed for logarithmic matching algorithm and RSSI weighted matching algorithm, combined with the traditional weighted K nearest neighbor algorithm (WKNN), with improved the rule of choose points, proposed an improved synthesis algorithm. Finally, by using self-developed simulation software to test, and analyzing to the results of the simulation are verify the positioning precision of the improved synthesis algorithm is better than the typical WKNN algorithm.Finally, we carried out a test in a real environment by using self-developed positioning software, notebook computer and wireless routers to test WKNN algorithm and the improved synthesis algorithm respectively. Through analyzing the results, verify the positioning precision of the improved synthesis algorithm is better than the typical WKNN algorithm, it is consistent with the result of the third chapter.