Robust Localization in Cellular Networks via Reinforced Iterative M-Estimation and Fuzzy Adaptation

Abstract

We consider robust mobile location estimation using range measurements from multiple base stations under probabilistically mixed line-of-sight (LOS)/non-LOS (NLOS) conditions in a cellular network, where the NLOS statistics and occurrence probabilities (NOPs) are unknown. We develop new algorithms for achieving desirable performance under these challenging conditions. First, we propose a measurement preprocessing (MPP) scheme for dynamically bounding the NLOS influence on raw range measurements. The mobile positioning problem is cast as the state estimation of an MPP-based system model. Next, we introduce extended Kalman filter (EKF)-based reinforced robust regression and develop a gradient descent-type iterative M-estimation procedure with ensured stability, resulting in a reinforced M-estimation-based robust EKF (M-REKF). Subsequently, we present a fuzzy-tuning M-REKF (FTM-REKF) incorporating adequate fuzzy adaptation of measurement noise covariances in the M-REKF. A performance analysis provides insight into how adequate fuzzy rules can be established methodically for the FTM-REKF. Simulations demonstrate that despite varying NOPs and NLOS statistics, the FTM-REKF and M-REKF stably yield satisfactory location estimates of moving and stationary targets, with the former outperforming the latter, and they can meet the Enhanced 911 positioning requirements. Moreover, the proposed algorithms significantly outperform several salient iterative M-estimation-based EKFs, and their MPP-based counterparts.