Performance Assessment of Adaptive Kalman Filter-Based GNSS PVT and Integrity in Dense Urban Environment

Abstract

This paper argues that an adaptive extended Kalman filter (EKF) improves the performance of global navigation satellite system (GNSS) position, velocity, and time (PVT) and their integrity information by single point positioning in dense urban environments without sensor aiding or coupling. One of the most important, as well as difficult problems of single point positioning in urban environments, is the negative impact of non-line-of-sight (NLOS) signals, which cause significant and unexpected measurement errors that degrade the PVT performance of conventional positioning methods. To reduce this impact, an adaptive EKF was implemented for single point positioning. Six laps of test drives were conducted to prove that the adaptive EKF drastically reduces the position and velocity errors and removes outliers when compared to a conventional EKF. Moreover, the adaptive covariance matrix contributed to the integrity information. The horizontal and vertical protection levels (HPL/VPL) of both the position and velocity computed by the covariance matrix of the state vector were degraded for the conventional EKF; however, when they were measured using the adaptive EKF, the degradation was restrained. Thus, the adaptive EKF could be proposed as a simple and effective technique to reduce the negative impact of NLOS and to improve the GNSS performance in heavy-NLOS environments for any type of GNSS receiver.