Initial orbit determination of BDS-3 satellites based on new code signals

For the two newly launched satellites(PRN number 27 and 28) of the future global BeiDou navigation satellite system(BDS-3), there is no available broadcast ephemeris data and other initial orbit information, but the initial orbit is the fundamental of the comprehensive analysis of the satellites and their signals. Precise orbit determination(POD) also requires determination of a priori initial value with a certain precision in order to avoid problems such as filter divergence during POD. Compared with the Newton iteration method, which relies on the initial value, this study utilizes the Bancroft algorithm to directly solve the nonlinear equations with the advantage of numerical stability. The initial orbits of these two satellites are calculated based on new code signals, and their results are analyzed and discussed. The experimental results show that, with the exception of very few epochs, when the new code signal is utilized, the median and robust variance factor of the observed residuals computed using pseudo-range observations and the solved initial orbits are less than 4 and 2 m, respectively. It also shows that this solution can be used for rapid initial orbit recovery after maneuvers of the new BeiDou satellites.

Variation of satellite transponder delay

For precise orbit determination of geosynchronous earth orbit(GEO)satellites using transfer ranging observations,it is generally assumed that the variation of the satellite transponder delay is very small and that it can be solved as a constant parameter together with satellite orbit parameters.However,this assumption is too general and it reduces the accuracy of orbit determination for GEO satellites.To study and analyse the impact of the satellite transponder delay on GEO satellites orbit determination,two schemes were proposed.First,the satellite transponder delay was eliminated by forming single-difference observations between two ground stations;second,the satellite transponder delay was described as a constant parameter.The preliminary results demonstrate a difference of about1–2 m between the two schemes when used for precise orbit determination of GEO satellites.By fixing the GEO satellite orbit and other relevant parameters estimated by single-difference model,we inversed the instantaneous transponder delay from non-difference observation.It was found that the satellite transponder delay has a distinct diurnal variation,with an amplitude of 3–4 m.The findings of this paper are helpful in establishing an accurate model of satellite transponder delay and in improving the accuracy of GEO satellites orbit determinations and predictions.

Crowdsourcing RTK:a new GNSS positioning framework for building spatial high-resolution atmospheric maps based on massive vehicle GNSS data

High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution(AR)in precise positioning.However,traditional real-time precise positioning frameworks(i.e.,NRTK and PPP-RTK)depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network.This results in limited public appeal.With the mass production of autonomous driving vehicles,more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps.In this study,we propose a new GNSS positioning framework that relies on dual base stations,massive vehicle GNSS data,and crowdsourced atmospheric delay correction maps(CAM).The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way.Specifically,the map consists of between-station single-differenced ionospheric and tropospheric delays.We introduce the whole framework of CAM initialization for individual vehicles,on-cloud CAM maintenance,and CAM-augmented user-end positioning.The map data are collected and preprocessed in vehicles.Then,the crowdsourced data are uploaded to a cloud server.The massive data from multiple vehicles are merged in the cloud to update the CAM in time.Finally,the CAM will augment the user positioning performance.This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other.We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales.We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.