Massive Differencing of GNSS Pseudorange Measurements

Abstract

Global Navigation Satellite Systems (GNSS) is a popular positioning solution able to provide high accuracy, integrity, reliability and high coverage. GNSS performance may be enhanced through aiding systems such as Differential GNSS (DGNSS), which aims to mitigate disruptive sources of error by using corrections sent from a reference station. In this paper, we investigate a method that provides performance results comparable to those by DGNSS without the need for a reference station. We propose the Massive User-Centric Single Difference (MUCSD) algorithm, which leverages a set of collaborative receivers exchanging observables and, potentially, their noisy estimates of position and clock bias. MUCSD is implemented as an iterative weighted least squares (WLS) estimator and its lower accuracy bound, as given by the Cramér-Rao Bound (CRB), is derived as a performance benchmark for the WLS solution. Simulation results are provided as a function of the number of collaborative users and the exchanged information uncertainty. Results show that, without having to access costly-to-maintain reference stations, MUCSD asymptotically outperforms DGNSS as the number of collaborative receivers grows.