A Mobile Localization Method in Smart Indoor Environment Using Polynomial Fitting for Wireless Sensor Network. (7th January 2020)

• Record Type: Journal Article
• Authors: Wang, Yan; Yan, Yang; Li, Zhengjian; Cheng, Long
• Other Names: Ahmed Ghufran Academic Editor.
• Is Part Of: Journal of sensors. Volume 2020(2020)
• Publication Date: 2020-01-07
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2020/6787252
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : The main factor affecting the localization accuracy is nonline of sight (NLOS) error which is caused by the complicated indoor environment such as obstacles and walls. To obviously alleviate NLOS effects, a polynomial fitting-based adjusted Kalman filter (PF-AKF) method in a wireless sensor network (WSN) framework is proposed in this paper. The method employs polynomial fitting to accomplish both NLOS identification and distance prediction. Rather than employing standard deviation of all historical data as NLOS detection threshold, the proposed method identifies NLOS via deviation between fitted curve and measurements. Then, it processes the measurements with adjusted Kalman filter (AKF), conducting weighting filter in the case of NLOS condition. Simulations compare the proposed method with Kalman filter (KF), adjusted Kalman filter (AKF), and Kalman-based interacting multiple model (K-IMM) algorithms, and the results demonstrate the superior performance of the proposed method. Moreover, experimental results obtained from a real indoor environment validate the simulation results.