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

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