Abstract

Although ionosphere-free (IF) combination is usually employed in long-range precise positioning, in order to employ the knowledge of the spatiotemporal ionospheric delays variations and avoid the difficulty in choosing the IF combinations in case of triple-frequency data processing, using uncombined observations with proper ionospheric constraints is more beneficial. Yet, determining the appropriate power spectral density (PSD) of ionospheric delays is one of the most important issues in the uncombined processing, as the empirical methods cannot consider the actual ionosphere activities. The ionospheric delays derived from actual dual-frequency phase observations contain not only the real-time ionospheric delays variations, but also the observation noise which could be much larger than ionospheric delays changes over a very short time interval, so that the statistics of the ionospheric delays cannot be retrieved properly. Fortunately, the ionospheric delays variations and the observation noise behave in different ways, i.e., can be represented by random-walk and white noise process, respectively, so that they can be separated statistically. In this paper, we proposed an approach to determine the PSD of ionospheric delays for each satellite in real-time by denoising the ionospheric delay observations. Based on the relationship between the PSD, observation noise and the ionospheric observations, several aspects impacting the PSD calculation are investigated numerically and the optimal values are suggested. The proposed approach with the suggested optimal parameters is applied to the processing of three long-range baselines of 103 km, 175 km and 200 km with triple-frequency BDS data in both static and kinematic mode. The improvement in the first ambiguity fixing time (FAFT), the positioning accuracy and the estimated ionospheric delays are analysed and compared with that using empirical PSD. The results show that the FAFT can be shortened by at least 8% compared with using a unique empirical PSD for all satellites although it is even fine-tuned according to the actual observations and improved by 34% compared with that using PSD derived from ionospheric delay observations without denoising. Finally, the positioning performance of BDS three-frequency observations shows that the averaged FAFT is 226 s and 270 s, and the positioning accuracies after ambiguity fixing are 1 cm, 1 cm and 3 cm in the East, North and Up directions for static and 3 cm, 3 cm and 6 cm for kinematic mode, respectively.

Highlights

  • In this study, we focus on the long-range triple-frequency Real-Time Kinematic (RTK), only BeiDou Navigation Satellite System (BDS) triple frequency data are processed and the conclusions should be applicable to the other system

  • For long-range precise positioning, instead of ionosphere-free observation, the uncombined observations are suggested, especially for triple-frequency data processing, in which the ionospheric delays are generally estimated as a random walk process with proper spatiotemporal constraint represented by the process power spectral density (PSD)

  • The ionospheric delays derived from the phase observations containing the actual ionospheric delays variation, it is contaminated by the observation noise which is much larger than ionospheric variation in a very short time

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Summary

Introduction

Bock proposed a method of incorporating prior information on residual ionospheric effects in the form of weighted constraints in order to improve phase ambiguity resolution [19] It was successful in unifying the processing algorithm for short and long baselines by imposing ionospheric constraints according to baseline length. In the above-mentioned methods, the PSD of the ionospheric delays parameterized as a random walk process is usually obtained empirically or based on previous data It cannot always accurately describe the current status, especially if there are temporal and spatial variations of small scale [28]. The efficiency of the new approach is validated by comparing it with the existing ones before conclusions are drawn

Basic Observation Equations
Long-Range RTK Model
Parameter Estimation
Ionospheric Observation
Analysis of Ionospheric Delays Time Correlation
Ionospheric Smoothing Method
Experimental Data
Data Processing
Empirical Ionospheric Constraint
Satellite-Specified Ionospheric Contraint
Impact of the Smoothing Window Width
BDS Triple-Frequncy Long-Range RTK
Findings
Conclusions
Full Text
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