Comprehensive Evaluations of NLOS and Linearization Errors on UWB Positioning

Abstract

Currently, ultra-wide band (UWB) is adopted as a useful high-accuracy positioning technique in satellite-blocked areas. However, UWB’s positioning performance would be limited significantly because of non-line of sight (NLOS) errors. Additionally, the truncation errors in these linearization-based adjustments such as least squares (LS) and extended Kalman filter (EKF) would also visibly degrade UWB positioning accuracy. To overcome the impacts of NLOS errors and truncation errors, this paper introduced a robust-theory-based particle filter (RPF) into UWB positioning. In such a method, the IGG-III model and PF were adopted to limit the impacts of NLOS errors and truncation errors, respectively, by introducing a weight inflation factor and particle group. For comparison, the Bancroft, LS, EKF, unscented Kalman filter (UKF), cubature Kalman filter (CKF), PF, and RPF were also presented. Here, the influences of truncation errors were analyzed by comparing the results based on LS and EKF with those calculated by UKF, CKF, and PF. The impacts of NLOS errors were evaluated by making a comparison between the results of PF and RPF. Results based on a set of simulated UWB data and a group of experiment UWB data demonstrated that the RPF can significantly avoid the positioning errors caused by both truncation errors and NLOS errors. In general, position improvements percentages of 57.2%, 52.7%, 39.6%, 38.2%, 26.6%, and 20.4% can be obtained by RPF compared to those calculated by Bancroft, LS, EKF, UKF, CKF, and PF, respectively. As a comparison, the truncation error would lead to about 8.1%, 10.1%, and 33.2% accuracy decrease in the north, east, and vertical directions on average. Such accuracy-decrease rates caused by NLOS were 6.1%, 5.2%, and 25%.

An Ultra-Wideband Indoor Localization Algorithm with Improved Cubature Kalman Filtering Based on Sigmoid Function

“…The volume criterion is used to pass a series of point sets with a nonlinear function and then approximate the posterior mean and variance of the nonlinear function based on the weighted summation [22]. When the standard CKF algorithm is used to calculate the volume points, Cholesky matrix decomposition is usually used to solve the estimated covariance matrix P k−1|k−1 , P k|k−1 .…”

Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects

“…The authors also emphasize that incorrect error quantification may affect the system's filtering capacity (position estimation) for a longer time. The average position error of their method under line-of-sight (LOS) conditions with a high density of anchors is about 0.5 m. The authors of [48] presented the weaknesses of the Kalman filter and its various modifications (extended, unscented, and cubature Kalman filters) both in simulation and real tests. These include, among others, the lack of gross error resistance, dependency on the initial values, and problems with the linearization and stability of the algorithms.…”