Adaptive indoor positioning method based on direction discrimination and device conversion. (1st April 2020)

• Record Type: Journal Article
• Authors: Li, Shirong; Fu, Maosheng; Zhu, Xuemei; Zhang, Fenghui; He, Fugui
• Is Part Of: IET wireless sensor systems. Volume 10:Number 2(2020)
• Page Start: 88
• Page End: 95
• Publication Date: 2020-04-01
• Subjects: regression analysis -- principal component analysis -- support vector machines -- least squares approximations -- indoor navigation -- piecewise polynomial techniques -- telecommunication computing; direction discrimination model -- device conversion model -- RSS -- occlusion direction -- adaptive indoor positioning method -- received signal strength -- least square piecewise polynomial algorithm -- principal component analysis -- offline data collection workload -- least square support vector regression algorithm; Wireless sensor networks -- Periodicals; 681.2
• DOI: 10.1049/iet-wss.2019.0079
• Languages: English
• ISSNs: 2043-6386

Abstract

Abstract : Received signal strength (RSS) greatly differs due to the different occlusion directions and receiving device heterogeneity. It greatly affects the positioning accuracy. In this study, an adaptive indoor positioning method based on the direction discrimination and device conversion is proposed to solve these problems. This method is mainly composed of three parts: direction discrimination, device conversion and positioning models. First, the direction discrimination model can reduce the impact of a user's body occlusion. Best access points can be selected by principal component analysis to adapt to different directions and areas. Secondly, a device conversion model is used to reduce high offline work due to device heterogeneity. RSS of other devices can be converted to the value of one fixed device by least squares piecewise polynomial algorithm, without increasing the offline data collection workload. Finally, the results can be obtained by the positioning model. The problems of high dimensionality and non‐linearity can be solved by the least squares support vector regression algorithm. Experimental results show that the proposed method can solve the problems of occlusion direction and device heterogeneity. The engineering applicability of positioning system can also be greatly improved.