Research On Multi-information Fusion Positioning Method

Location based service(LBS)is a value-added service mode based on positioning technology.With the emergence of wireless communication,intellisense technology and the popularity of mobile intelligent terminal,a variety of demand positioning services,such as scenic spots for sightseeing,shopping navigation,pedestrian tracking,smart home,etc.,quickly promote the development of different positioning systems and related technologies to provide location information for individual customer.Inertia sensors in smartphones can be used to realize Dead Reckoning positioning with the methodology offering by Pedestrian Dead Reckoning(PDR).At present,PDR is well supported by mobile devices,which,however,comes with a large inertia cumulative error in positioning.Wi-Fi covers a wide range and can meet the needs of most indoor positioning services.Although Wi-Fi based positioning technology will not cause the cumulative error,its signals are vulnerable to environmental interference,resulting in large fluctuations in positioning results.Research on Wi-Fi/PDR fusion positioning optimization will help improve the stability and reliability of the positioning system.However,there are a series of bottlenecks in these technologies,such as the determination of the initial positioning state,the fusion positioning strategy under complex indoor scenes,and the implementation and application of the positioning system.To solve these problems,the paper studies the fusion positioning systems and technologies based on Wi-Fi/PDR.In this paper,the deviation of initial value of extended Kalman filtering system may result in distinct difference of the state filtering.Therefore,quantitative analysis of the initial state problem of the fusion positioning system has been carried out in this paper,and an extended Kalman filtering fusion algorithm with adaptive initial state is proposed.Through the initial Wi-Fi location points with Kalman filter to obtain the accurate initial position and heading angle,the method can well adapt to different initial state.Finally,an improved extended Kalman filtering algorithm based on dynamic system parameters measured by the RSSI Euclidean distance of adjacent states is designed to reduce the influence of Wi-Fi fluctuation on the EKF system.The experimental results show that the proposed fusion positioning system can not only weaken the phenomenon of Wi-Fi instability,but also solve the cumulative error of the PDR system based on inertial navigation and the positioning error caused by the initial position deviation.The high degree of accuracy is maintained in different experiments.For the requirements of indoor pedestrian tracking,a pedestrian tracking system based on Wi-Fi/PDR fusion positioning is designed and implemented in this paper.In general,the system adopts the architecture of separating the data acquisition module and positioning engine.To realize the visualization of the positioning information and achieve the track of the target object,the data acquisition module mainly adopts the Android platform,and the positioning engine is built by the Web application.This paper analyses the process of the system construction,and completes the fusion positioning system through the Android platform and Web application.The fusion positioning system has been built based on the PDR algorithm and the Wi-Fi location fingerprint algorithm.According to the expected requirements of the system function test,the results show that the system has the characteristic of high stability in the actual location scene.