Research On Indoor Positioning Algorithm Based On Multidimensional Information Fusion

Location-based services are becoming increasingly important in today’s life.While outdoor positioning technology is becoming more mature,indoor positioning technology is still in the development stage.Many single positioning technologies have been proposed,such as Bluetooth Low Energy(BLE)positioning,Wi Fi positioning,and Ultrawideband(UWB)positioning.However,various single positioning technologies always fail to balance cost,accuracy and universality.Therefore,there is great potential for the development of indoor positioning technology that can balance cost,accuracy and universality.In order to make up for the shortcomings of single positioning technology,the thesis adopts the method of fusing multi-dimensional information to improve the positioning effect.Considering the cost and power consumption issues,BLE was chosen as the basic positioning technology.Then,particle filtering was used to fuse Received Signal Strength Indicator(RSSI),map structure information,sensor data and historical coordinate data to achieve a multi-dimensional information fusion indoor positioning algorithm.The main research contents of this thesis are as follows:Firstly,the change characteristics of RSSI were studied and analyzed,including the change of packet loss rate with distance and fluctuation change.Then,the trilateration algorithm based on RSSI only and particle filtering algorithm were experimentally analyzed respectively.The results show that considering only one dimension of RSSI cannot achieve good positioning effect.Therefore,the thesis designs an indoor map system that can clearly represent indoor map structure information for constraining particle transfer.At the same time,the concept of region is introduced and a dynamic particle number allocation method based on map region is proposed.In this way,RSSI and map information are more deeply fused to achieve better positioning effect than traditional algorithms.Compared with the traditional trilateration positioning effect,the root mean square error was reduced by 11.21%.Then the thesis uses sensors of smart mobile devices to estimate the number of steps,step length and heading of pedestrians during positioning based on acceleration data and magnetometer data on the basis of previous algorithms.Then according to the estimated relative trajectory to determine the specific direction of particle transfer,further optimize the state transfer of particles to achieve a positioning algorithm that fuses sensor data.Compared with traditional trilateration positioning effect,average error was reduced by22.75%.Then on the basis of previous analysis of frequently occurring and obvious characteristic positioning errors a correction algorithm for state estimation was proposed which deeply fused map information sensor data and historical coordinate data.Finally a fusion positioning algorithm better than previous algorithms was achieved with an average absolute error reduction of about 34.23% compared to traditional trilateration.Finally the thesis builds and deploys an actual positioning system using the proposed multi-dimensional information fusion indoor positioning algorithm which achieves good positioning effect in actual indoor scenarios proving that this algorithm has certain engineering practical value.