Research And Implementation Of Twice-matching Positioning In Sparse Scene

In recent years,with the advancement of communication technology,the research on indoor location services has developed rapidly.Affected by limitations of GPS and constraints of firmware configuration,the fingerprint positioning methods based on signal energy has become an important direction in indoor positioning technology research.However,the complexity of the indoor environment makes it easy for wireless signals to cause multipath effects.It is difficult to provide accurate mathematical models for signal transformations in different scenarios.At the same time,although the fingerprint method avoids signal carrier differences and content details,the positioning accuracy is inevitably affected by the speed and efficiency of offline fingerprint database construction.Thesis proposes improved algorithms from the offline database construction stage and the online positioning stage to reduce the overhead of fingerprint database construction while improving the positioning accuracy.The main work and innovations are as follows:(1)Considering the problem that the construction of fingerprint database in indoor positioning research requires a lot of manpower and time for signal acquisition,this paper proposed a fingerprint database expansion algorithm based on dual beacon mechanism and radial basis neural network.This algorithm can flexibly adjust the sampling ratio of beacon points according to the needs of the scene,and obtain the lowest sampling overhead under the premise of ensuring the accuracy of prediction.The algorithm uses radial basis neural network to mine the deep connection between the position of the beacon and the signal to predict the signal strength of the position to be estimated,and achieve the purpose of expanding the fingerprint database.Tested by actual scene,on the premise of ensuring the accuracy of prediction,The proposed algorithm expands the fingerprint database by 140%.Through experimental verification,the average prediction error is reduced by more than 12%compared with the existing algorithms.(2)Considering the problem that the traditional area division method in fingerprint positioning cannot guarantee the accuracy of area matching,thesis proposed a fuzzy matching algorithm.The matching range can be dynamically adjusted,improves the positioning speed and decreases the defects of mismatch caused by area division.In the position calculation stage,the algorithm uses multivariate similarity coefficients and introduces environmental parameters to redistribute the weights of neighboring points.This algorithm overcomes the adverse effects of fluctuation in environment and equipment and reduce positioning errors.Tested by actual scene,on the premise of ensuring the accuracy of matching,the proposed algorithm reduces the amount of fingerprint matching by more than 60% compared with global matching,and was experimentally verified that the positioning accuracy is improved more than 17%,compared with the existing algorithms.(3)In order to combine the research content with practical engineering applications,thesis designs and implements a BLE-based location service system.thesis transplants the fingerprint database expansion method of dual beacon and radial basis in the sparse scene and the multiple similarity positioning algorithm under dynamic fuzzy matching into the system.The system uses mobile APP mobile phone scene fingerprint data and user fingerprint data,and deploys the positioning algorithm to the cloud server in the background,and uses the powerful computing power of the cloud server to realize location estimation.Finally,analyze and display location data personalized for users.The thesis provides practical application scenarios for the researched algorithms through enterprise project development.The system is implemented in the form of "client + cloud",providing a set of digital and intelligent human flow and location tracking management system for indoor scenes.