Abstract

Global Navigation Satellite System precise positioning using carrier phase measurements requires reliable ambiguity resolution. It is challenging to obtain continuous precise positions with a high ambiguity fixing rate under a wide range of dynamic scenes with a single base station, thus the positioning accuracy will be degraded seriously. The Forward–Backward Combination (FBC), a common post-processing smoothing method, is simply the weighted average of the positions of forward and backward filtering. When the ambiguity fixing rate of the one-way (forward or backward) filter is low, the FBC method usually cannot provide accurate and reliable positioning results. Consequently, this paper proposed a method to improve the accuracy of positions by integrating forward and backward AR, which combines the forward and backward ambiguities instead of positions—referred to as ambiguity domain-based integration (ADBI). The purpose of ADBI is to find a reliable correct integer ambiguities by making full use of the integer nature of ambiguities and integrating the ambiguities from the forward and backward filters. Once the integer ambiguities are determined correctly and reliably with ADBI, then the positions are updated with the fixing ambiguities constrained, in which more accurate positions with high confidence can be achieved. The effectiveness of the proposed approach is validated with airborne and car-borne dynamic experiments. The experimental results demonstrated that much better accuracy of position and higher ambiguity-fixed success rate can be achieved than the traditional post-processing method.

Highlights

  • Post-processed, relative Global Navigation Satellite System (GNSS) kinematic positioning is a widely-used technology to provide high-accuracy trajectory determination in many dynamic applications, such as airborne vector gravimetry and Mobile Mapping Systems (MMS) (Kreye and Hein 2003; Senobari 2010; Petrovic et al 2015)

  • In “The processing scheme of the ambiguity domainbased integration (ADBI)” section, we describe the six-step procedure of the ADBI method

  • NovAtel technical documents specify that this commercial software can achieve 2–6 cm positioning accuracy when the baseline is shorter than 130 km (Gao et al 2015)

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Summary

Introduction

Post-processed, relative Global Navigation Satellite System (GNSS) kinematic positioning is a widely-used technology to provide high-accuracy trajectory determination in many dynamic applications, such as airborne vector gravimetry and Mobile Mapping Systems (MMS) (Kreye and Hein 2003; Senobari 2010; Petrovic et al 2015). A Kalman filter (KF) is commonly applied to estimate the navigation parameters in such post-processed kinematic applications (Kalman 1960; Herring et al 1990; Chen 1998; Yang 2010). Precise GNSS positioning relies on very precise carrier phase observables with fixed ambiguities (Han 1997; Cai et al 2007). The accuracy of estimated positions with carrier phase observations relies upon the performance of ambiguity resolution (AR) (Teunissen 2003; Li et al 2014a). For differential GNSS, if the baseline is long, or the observation environment

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