Time-correlated Window Carrier-phase Aided GNSS Positioning Using Factor Graph Optimization for Urban Positioning

TL;DR

This paper introduces a novel GNSS positioning approach that leverages time correlation of measurements within a window and factor graph optimization, significantly improving urban positioning accuracy and robustness.

Contribution

It proposes using window-based carrier-phase measurements and factor graph optimization to enhance GNSS positioning in urban environments, addressing outliers and measurement correlation.

Findings

Achieved mean positioning errors of 1.76m and 2.96m in urban canyons.

Demonstrated improved accuracy with low-cost smartphone GNSS receivers.

Outperformed several existing GNSS positioning methods.

Abstract

This paper proposes an improved global navigation satellite system (GNSS) positioning method that explores the time correlation between consecutive epochs of the code and carrier phase measurements which significantly increases the robustness against outlier measurements. Instead of relying on the time difference carrier phase (TDCP) which only considers two neighboring epochs using an extended Kalman filter (EKF) estimator, this paper proposed to employ the carrier-phase measurements inside a window, the so-called window carrier-phase (WCP), to constrain the states inside a factor graph. A left null space matrix is employed to eliminate the shared unknown ambiguity variables and therefore, correlated the associated states inside the WCP. Then the pseudorange, Doppler, and the constructed WCP measurements are integrated simultaneously using factor graph optimization (FGO) to estimate the state of the GNSS receiver. We evaluated the performance of the proposed method in two typical urban canyons in Hong Kong, achieving the mean positioning error of 1.76 meters and 2.96 meters, respectively, using the automobile-level GNSS receiver. Meanwhile, the effectiveness of the proposed method is further evaluated using a low-cost smartphone level GNSS receiver and similar improvement is also obtained, compared with several existing GNSS positioning methods.