| The Internet of Things and mobile devices are gradually becoming more and more popular,the demand for services which are based on the locations is increasing.Positioning accuracy,technical cost and algorithm efficiency determine whether the indoor positioning system can be applied in real life.The indoor location method based on position fingerprint algorithm has relatively high positioning accuracy,but due to the signal interference in the indoor environment,a single algorithm cannot fit the actual scene well,and the position fingerprint algorithm has the defects of outliers with large errors as well as high sampling spending.To the opposite,the Pedestrian Dead Reckoning(PDR)algorithm which presents relatively stable positioning results has low positioning spending,but there is a problem that the initial position cannot be determined and there are accumulated errors.At present,the universal mobile terminal is the mobile phone device,the indoor positioning technology based on the mobile phone platform needs to be improved.In response to the above problems,thesis proposes a multi-source fusion positioning algorithm.On this basis,thesis designs and implements an indoor positioning system.The main research contents are as follows:Aiming at the problem of high sampling cost in the offline phase of the location fingerprint positioning method,a Crystal-shape i Beacon Placement(CIP)layout strategy is studied.Based on the CIP layout,the traditional location fingerprint positioning algorithm is improved.When sampling in the offline phase,the fingerprint information of a representative area is used to replace the fingerprint information of the entire positioning area.When positioning in the online phase,the adjacent i Beacon of the i Beacon with the largest signal strength value is selected as the reference point,and the position is calculated by the WKNN algorithm.The simulation results show that the location accuracy of the indoor location method based on position fingerprint algorithm is similar to that of the traditional layout,but the sampling spending is reduced,and the complexity of the matching algorithm is reduced fromO(n~2)to O(n).The experimental results show that the average positioning error is1.72m,which can be achieved to0.80m by increasing the sampling density.Aiming at the problem of unstable positioning results of the position fingerprint positioning method,the improved position fingerprint algorithm and the PDR algorithm are combined.The initial positioning coordinates of the position fingerprint are set as the initial position of the PDR algorithm,and the product of time and distance is used as the threshold.When the threshold is reached,the positioning results of the two algorithms are fused through the extended kalman filter,and the fusion result is updated to the initial position of the PDR algorithm.Experimental results show that the positioning accuracy of the fusion algorithm is35%higher than that of a single positioning algorithm.In view of the low-cost and high-precision positioning requirements,thesis designs an indoor positioning system.The system has main functional modules such as Bluetooth scanning,real-time positioning,map matching and path planning.The real-time positioning module is implemented by the improved fusion algorithm.The Map matching module is implemented by the employment of a map matching algorithm based on the hidden Markov model,and the path planning module is implemented by the employment of the A star algorithm.The system is implemented on the Android platform and the wechat applet platform,which is deployed and tested in an underground parking lot located at the urban area.The actual scene shows that the system function modules are operating normally. |
PDF Full Text Request