Time-of-arrival source localization based on weighted least squares estimator in line-of-sight/non-line-of-sight mixture environments

Abstract

In this article, we propose a line-of-sight/non-line-of-sight time-of-arrival source localization algorithm that utilizes the weighted least squares. The proposed estimator combines multiple sorted measurements using the spatial sign concept, Mahalanobis distance, and Stahel-Donoho estimator, that is, assigning less weight to the samples as they are far from the center of inlier distribution. Also, the eigendecomposition Kendall's t covariance matrix is utilized as the scatter measure instead of the conventional median absolute deviation. Thus, the adverse effects by outliers can be attenuated effectively. To validate the superiority of the proposed methods, the root mean square error performances are compared with that of the existing algorithms via extensive simulation.

Nonlinear programming-based ranging optimization for three-dimensional indoor time of arrival localization

“…The basic indoor TOA positioning algorithms include Taylor series-based estimation, 11,12 geometric relations-based algorithm, 13,14 maximum likelihood estimation, 14,15 and least square (LS). [16][17][18][19] Some improved localization algorithms, which are based on these basic ones, were proposed in the existing literatures to reduce the effect of large ranging error caused by NLOS. LS is one of the most popular algorithms adopted TOA-based indoor positioning due to its low computational complexity.…”

“…LS is one of the most popular algorithms adopted TOA-based indoor positioning due to its low computational complexity. Park and Chang 16 proposed an LOS/NLOS TOA source localization algorithm that utilizes the weighted least squares (WLS). However, the exact choice of the weighted value directly affects the positioning accuracy, and the exact weighting value is difficult to obtain.…”