Study On Wifi Indoor Location Techniques Based On IOS

With the continuous development of mobile Internet, a variety of location-based services applications have become more and more close to people’s daily lives. However, due to signal blocking issues, they can not meet the needs of indoor positioning. In order to solve the issues of indoor positioning, there has been a large number of indoor positioning solutions. A variety of indoor positioning technology have their own advantages and disadvantages on positioning accuracy and deployment costs. As a way of positioning, Inertial positioning technology does not depend on the external environment. It has some features of wide application range, convenient deployment and low cost. With the development of Micro-electro mechanical Systems, more and more intelligent devices not only have a high-speed computing capability, but also equipped with inertial sensors. In this background, the mobile terminal by means of inertial sensors to achieve indoor positioning function can be achieved.Combining traditional WIFI indoor positioning technology and inertial sensor technology of mobile terminals, this paper proposes a new WIFI indoor positioning methods which assisted by inertial sensor positioning, and implemented an indoor positioning system for iOS. The main research work is as follows:1、Getting the data from accelerometer, gyroscope and electronic compass with Core Motion frame on iOS platform. Processing The Z-axis acceleration with moving average, using the peak detector method to get the steps of walking. Using constant step model to estimate the distance traveled. Combined with heading data from electronic compass to obtain a more accurate trajectory. By converting inertial positioning coordinates with real geographic coordinates, to determine the scope of the positioning.2、According to the results obtained by the inertial positioning algorithm, to sort out the fingerprint data from the position in the fingerprint database, which are in inertial positioning error range. After that, processing WIFI wireless signal which obtained in real time with Gaussian filter, then matching to the existing location fingerprint database to achieve a more precise positioning.3、In the real scene, test the positioning accuracy and positioning efficiency of the system. The real scene experiment proves that this method greatly reduces the computation time against the conventional fingerprint positioning algorithm, and it has a higher accuracy.