Fast PPP Ambiguity Resolution Using a Sparse Regional Reference Network

Abstract

Precise point positioning real-time kinematic (PPP-RTK) can achieve fast ambiguity resolution and precise positioning with the satellite fractional cycle biases (FCBs) and atmospheric correction derived from a dense regional reference network. However, the interpolated atmospheric corrections based on a sparse reference network with inter-station distances of more than 100 km are not precise enough to facilitate reliable PPP ambiguity resolution. In this contribution, a new method is proposed for fast PPP ambiguity resolution within a sparse regional reference network. First, code biases, FCBs, biased ionospheric and tropospheric delays at the reference stations are estimated with known positioning using the regional sparse reference network data. Then, the biased ionospheric and tropospheric corrections at a user station are generated using a distance-based linear interpolation of the ionospheric and tropospheric delays available at the reference stations. To strengthen the observation model, the interpolated ionospheric and tropospheric delays derived from the sparse network are all considered as pseudo-observations with a given variance-covariance matrix which will be adaptively estimated according to the reference network density to describe the level of the constraint strength. To get a realistic constraint variance, atmospheric constraint variance is estimated with a certain window length. By a proper tuning of the variance-covariance matrix applied for the atmospheric pseudo-observations, the method can adapt any scale of the regional reference network. The ambiguity fixing performance and the resulted position accuracy are assessed with medium and large reference networks. The validation confirms that the new strategy can fix ambiguity within 1 min for a medium network and within 7 min for a larger network while provide centimeter-level positioning solution ambiguity with 20.8 s for a medium network and within 71.9 s for a larger network.