Mobile Localization in Non-Line-of-Sight Using Constrained Square-Root Unscented Kalman Filter

摘要

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 thiswork. To this end, we develop a constrained form of square root unscentedKalman filter (SRUKF), where the sigma points of the unscented transformationare projected onto the feasible region by solving constrained optimizationproblems. The feasible region is the intersection of several discs formed bythe NLOS measurements. We show how we can reduce the size of the optimizationproblem and formulate it as a convex quadratically constrained quadraticprogram (QCQP), which depends on the Cholesky factor of the \textit{aposteriori} error covariance matrix of SRUKF. As a result of thesemodifications, the proposed constrained SRUKF (CSRUKF) is more efficient andhas better numerical stability compared to the constrained UKF. Throughsimulations, we also show that the CSRUKF achieves a smaller localization errorcompared to other techniques and that its performance is robust under differentNLOS conditions.