Research On Indoor Positioning Algorithm Based On WIFI Position Fingerprint

With the rapid development of mobile network technology,location-based services have received more and more attention from many people,which has led to more and more attention to positioning technology,especially indoor positioning technology.Due to the wide WIFI coverage and low cost,the WIFI location fingerprint method is currently an ideal positioning method.However,the traditional fingerprint algorithm has problems such as a large footprint of the fingerprint database,a high calculation volume,and the inability to select an access point(AP)with strong discrimination.The Quad-tree algorithm has high data insertion and search efficiency for evenly distributed data,but it is rarely used in the field of indoor positioning.Aiming at the above problems,this paper proposes an indoor positioning algorithm(Quad-tree search and reliable access point selection(QS-RAPS))based on Quad-tree search and reliable AP selection.The main work is as follows:Firstly,the affecting elements of WIFI signal propagation in the room are analyzed.Then,the properties of WIFI signals are studied from the time and space dimensions,and the collected RSSI(Received Signal Strength Indicator)values are filtered and compared.Secondly,aiming at the problems existing in traditional indoor positioning algorithms,this paper proposes a positioning algorithm based on QS-RAPS.The algorithm first constructs a reliable AP selection function.And in the fingerprint database establishment phase,the location fingerprints collected at different reference points are stored in the fingerprint database in the form of a grid structure.During the positioning phase,the positioning problem is transformed into a Quad-tree search Problem.It quickly search the cells of the neighborhood to filter out the neighborhood points,and use the weighted average idea to locate.Then,the QS-RAPS algorithm is compared with the WKNN algorithm at the theoretical level.The results show that the QS-RAPS algorithm can reduce the footprint and calculation of the fingerprint database,and improve indoor positioning accuracy.Thirdly,it sets up an experimental local area network and writes a WIFI signal acquisition software based on the Android platform.Considering the computing power of themobile terminal,the collected data is simulated on the MATLAB platform,and the matching and positioning results of the QS-RAPS algorithm are compared with the KNN,WKNN,and Bayes algorithms.The experimental results show that the error is 1.463 m,the average prediction time is 0.28 s,and the footprint of the fingerprint database is 68 KB.Compared with WKNN algorithms,the positioning accuracy is improved by 38.75%,the positioning efficiency is increased by 96.47%,and the footprint of the fingerprint database is reduced by15 %.The indoor positioning algorithm proposed in this paper from the perspective of data search and the nature of WIFI signals has been experimentally verified to effectively improve the efficiency and accuracy of indoor positioning,and reduce the footprint of the fingerprint database to meet the expectations of this paper.