The Research Of Algorithm On WLAN Indoor Positioning Based On Linear Discriminant Analysis And Gradient Boosting Decision Tree

With the implementation of the "Internet +" strategy,various types of smart devices have rapidly spread.People's lives are changed by various applications,which has led to more and more scenes that require indoor location information.Due to the building wall blocks signal propagation,GPS cannot be used for precise indoor positioning.Wireless local area networks(WLAN)technology standards have become increasingly completed and popularized in most buildings.The WLAN-based indoor positioning method only needs to use the WLAN network inside the building,which has low energy consumption and low error,and is a key research direction of indoor positioning technology.A new indoor positioning algorithm based on linear discriminant analysis(LDA)and gradient boosting decision tree(GBDT)is proposed in this paper,which mainly accomplishes the following five aspects:(1)The main achievements of researchers in the field of indoor positioning are summarized and the significance of research is clarified.The basic principles and technical standards of WLAN are expounded.The basic principles of geometric solution and location fingerprinting involved in WLAN indoor positioning are analyzed.The main algorithms included in these two methods are discussed,and the disadvantages of these algorithms are pointed out.This paper studies the location fingerprint method without additional hardware configuration and high positioning accuracy.(2)The characteristics of the received signal strength indication(RSSI)of access point(AP)and the factors affecting its accuracy are analyzed.Aiming at the problem that the RSSI changes irregularly with time,an LDA-based positioning feature extraction algorithm is proposed.The algorithm can make full use of the coordinate information of the reference point to effectively remove redundant features and abnormal signals in the fingerprint data.(3)After extracting the main features of location fingerprints by LDA,the method of calculating the coordinates of the fixed point by using the individual learning algorithm can not meet the requirements of positioning accuracy for the WLAN indoor positioning system.A GBDT positioning algorithm based on integrated learning is proposed.Firstly,the forward distribution algorithm is used to fit the negative gradient value of the loss function as an approximation of the error to a classification regression tree.Then,the additive model is used to linearly combine the iteratively generated classification and regression trees to generate the final GBDT positioning algorithm.The LDA positioning feature extraction algorithm is combined with the GBDT positioning algorithm to form the LDA-GBDT algorithm.(4)The validity of the LDA-GBDT positioning algorithm proposed in this paper is verified on the public data set.The experiment is carried out in many different aspects,such as the reserved fingerprint space dimension,the maximum depth of the classification regression tree,and the number of APs.The experimental results show that the positioning accuracy of the LDA-GBDT positioning algorithm proposed in this paper is 1.51 m in the public dataset scenario,which is more accurate than other WLAN indoor positioning algorithms.(5)In order to further verify the validity of the LDA-GBDT positioning algorithm in real scenes,this paper designs a WLAN indoor positioning software and uses the software to collect experimental data in real scenes.The experimental results show that the positioning accuracy of the proposed algorithm in the real scene is 1.19 m,which has obvious advantages compared with other positioning algorithms.