Vector Tracking Based on Factor Graph Optimization for GNSS NLOS Bias Estimation and Correction

Abstract

Position and location constitute critical context for Internet of Things (IoT) devices. Global navigation satellite systems (GNSSs) are the primary apparatus providing precise position and location information for IoT devices in outdoor environments. However, in dense urban areas, non-line-of-sight (NLOS) signals will induce large errors in GNSS pseudorange measurements due to the additional signal transmission paths. The vector tracking (VT) technique utilizing a Kalman filter (KF) to estimate navigation solutions has been investigated in NLOS detection, and its advantages have been demonstrated. However, the estimation of NLOS-induced bias has not been thoroughly investigated in the VT framework. In this article, we focus on the estimation and correction of NLOS-induced errors within the VT framework. First, graph optimization (GO) instead of a KF is incorporated with VT to optimize the estimation of navigation solutions. The NLOS-induced bias is then added to the VT state vector as the variable for real-time estimation. Compared with the KF-VT method, in GO-VT, the state transformation and the measurement model are regarded as constraints to optimize the state vector estimation. Hence, the GO-VT framework is more flexible than the KF approach in dealing with state vector changes. An iterative process is conducted to solve for the optimization results; a multiple-correlator scheme is employed in GO-VT to provide the initial values of the NLOS-induced bias. Three collected GPS L1 data sets (static and dynamic) are used to evaluate the proposed method. The statistical results support the conclusion that GO-VT with state augmentation achieves superior position estimation in urban areas.