Paper Info
Title
Mobile Localization in Non-Line-of-Sight Using Constrained Square-Root Unscented Kalman Filter
Abstract
Localization and tracking of a mobile node (MN) in non-line-of-sight (NLOS) scenarios, based on time-of-arrival (TOA) measurements, is considered in this paper. We develop a constrained form of a square-root unscented Kalman filter (SRUKF), where the sigma points of the unscented transformation are projected onto the feasible region by solving constrained optimization problems. The feasible region is the intersection of several disks formed by the NLOS measurements. We show how we can reduce the size of the optimization problem and formulate it as a convex quadratically constrained quadratic program, which depends on the Cholesky factor of the a posteriori error covariance matrix of the SRUKF. As a result of these modifications, the proposed constrained SRUKF (CSRUKF) is more efficient and has better numerical stability compared to the constrained unscented Kalman filter (UKF). Through simulations, we also show that the CSRUKF achieves a smaller localization error compared to other techniques and that its performance is robust under different NLOS conditions.
Year
DOI
Venue
2014
10.1109/TVT.2014.2339734
IEEE Transactions on Vehicular Technology
Keywords
DocType
Volume
kalman filters,covariance matrices,mobility management (mobile radio),quadratic programming,time-of-arrival estimation,cholesky factor,a posteriori error covariance matrix,constrained optimization problems,constrained square-root unscented kalman filter,convex quadratically constrained quadratic program,mobile localization,mobile node,non-line-of-sight,time-of-arrival measurements,unscented transformation,constrained kalman filter (kf),convex optimization,localization,non-line-of-sight (nlos)
Journal
64
Issue
ISSN
Citations 
5
0018-9545
8
PageRank 
References 
Authors
0.47
25
3
Name
Order
Citations
PageRank
Siamak Yousefi1131.90
Xiao-Wen Chang220824.85
Benoît Champagne351067.66