A Robust Unscented Kalman Filter applied to Ultra-wideband Positioning. (1st October 2020)

• Record Type: Journal Article
• Authors: Wang, Chuanyang; Ning, Yipeng; Li, Xin; Li, Haobo
• Is Part Of: International journal of image and data fusion. Volume 11:Number 4(2020)
• Page Start: 308
• Page End: 330
• Publication Date: 2020-10-01
• Subjects: Ultra-wideband -- robust unscented Kalman filter -- Mahalanobis distance -- hypothesis test; Image processing -- Periodicals; Multisensor data fusion -- Periodicals; Multisensor data fusion; Periodicals; 621.36705
• DOI: 10.1080/19479832.2020.1813816
• Languages: English
• ISSNs: 1947-9832

Abstract

ABSTRACT: Ultra-wideband (UWB) is well suited for indoor positioning due to its high resolution and good penetration through objects. As one of nonlinear filter algorithms, unscented Kalman filter (UKF) is widely used to estimate the position. However, UKF cannot resist the effect of outliers. The performance of the filter algorithm will be inevitably influenced. In this study, a robust UKF (RUKF) method accompanied by hypothesis test and robust estimation is proposed. Furthermore, the simulation and measurement experiments are performed to verify the effectiveness and feasibility of the proposed RUKF. Simulation experiment results are given to demonstrate that the RUKF can effectively control the influences of the outliers being treated as systematic errors and large variance random errors. When the outliers come from the thick-tailed distribution, the robust estimation does not play a role, and the RUKF does not work well. The measured experiment results show that the outliers will be generated in the non-line-of-sight environment whose impact is abnormally serious. The robust estimation can provide relatively reliable optimised residuals and control the influences of the outliers caused by gross errors. We can believe that the proposed RUKF is effective to resist the effects of outliers and improves the positioning accuracy.