Research On Indoor Positioning Algorithm Based On Machine Learning

In recent years,the research on indoor positioning in various fields has become increasingly popular.The indoor positioning service is mainly used in public areas such as airports,hospitals,supermarkets,and obtains the location information of the user through the interaction between the mobile terminal and the server.Due to the severe attenuation of signals and multipath effects,general outdoor positioning facilities(such as GPS)do not work effectively in buildings.In recent years,The development of WiFi is very fast,and indoor positioning using WiFi does not require additional hardware facilities.The hardware cost is low and the flexibility is high.WiFi-based fingerprint indoor positioning algorithm is one of the hot spots in the current research.This method composes the signal strength vector from several wireless access points to form a signal strength vector,and collects all offline signal strength vectors in the database to form a database.The positioning is performed by comparing the signal strength vector collected online with the vector in the offline database.Although the method is easy and valid,there are many problems such as large calculation amount and low positioning accuracy.The goal of this paper is to study the WiFi-based indoor positioning machine learning algorithm.The structure of this article has the following parts:(1)The paper firstly sum up the development prospects of indoor positioning in recent years,and analyzed the research results of predecessors.(2)Introduce different indoor positioning methods through a large number of literature research and data analysis.Then began to study different indoor positioning algorithms,compared the advantage and disadvantage of these algorithms based on various platforms,such as Bluetooth positioning,ultra-wide band positioning,WiFi positioning,etc.,and understand the merit and weakness of each indoor positioning technology.Then,the WiFi indoor positioning method is analyzed in depth.The paper compare different algorithms in WiFi positioning.The mainstream location fingerprint method is KNN algorithm,Bayesian algorithm and support vector machine learning algorithm.These position fingerprinting methods are analyzed.(3)Select the most commonly used KNN algorithm in WiFi fingerprint location,analyze the computational efficiency and positioning accuracy of the algorithm.Then we come up with an innovative method.The improvement is mainly divided into two categories.One method is to add a clustering algorithm before the KNN positioning algorithm,and improve and optimize the algorithm based on traditional clustering algorithm;the other method is to obtain the positioning coordinates after the KNN positioning algorithm and combine other algorithms for further optimization.(4)In order to verify the feasibility of the algorithm for experimental simulation,the improved algorithms mentioned in the paper are compared and analyzed.Finally,the paper summarizes the full text.