Precision-Aided Partial Ambiguity Resolution Scheme for Instantaneous RTK Positioning

Abstract

The use of carrier phase data is the main driver for high-precision Global Navigation Satellite Systems (GNSS) positioning solutions, such as Real-Time Kinematic (RTK). However, carrier phase observations are ambiguous by an unknown number of cycles, and their use in RTK relies on the process of mapping real-valued ambiguities to integer ones, so-called Integer Ambiguity Resolution (IAR). The main goal of IAR is to enhance the position solution by virtue of its correlation with the estimated integer ambiguities. With the deployment of new GNSS constellations and frequencies, a large number of observations is available. While this is generally positive, positioning in medium and long baselines is challenging due to the atmospheric residuals. In this context, the process of solving the complete set of ambiguities, so-called Full Ambiguity Resolution (FAR), is limiting and may lead to a decreased availability of precise positioning. Alternatively, Partial Ambiguity Resolution (PAR) relaxes the condition of estimating the complete vector of ambiguities and, instead, finds a subset of them to maximize the availability. This article reviews the state-of-the-art PAR schemes, addresses the analytical performance of a PAR estimator following a generalization of the Cramér–Rao Bound (CRB) for the RTK problem, and introduces Precision-Driven PAR (PD-PAR). The latter constitutes a new PAR scheme which employs the formal precision of the (potentially fixed) positioning solution as selection criteria for the subset of ambiguities to fix. Numerical simulations are used to showcase the performance of conventional FAR and FAR approaches, and the proposed PD-PAR against the generalized CRB associated with PAR problems. Real-data experimental analysis for a medium baseline complements the synthetic scenario. The results demonstrate that (i) the generalization for the RTK CRB constitutes a valid lower bound to assess the asymptotic behavior of PAR estimators, and (ii) the proposed PD-PAR technique outperforms existing FAR and PAR solutions as a non-recursive estimator for medium and long baselines.

Precision-Aided Partial Ambiguity Resolution Scheme for GNSS Attitude Determination

“…Model-driven (MD-PAR) methods base the subset selection I only on co-covariance matrix P â information, while a data-driven (DD-PAR) approach integrate also the float ambiguities vector â. However, the effect of projecting the resolved integer ambiguities into the position domain as criteria for the subset selection called precision-aided PAR scheme has been addressed in [17], [18] giving good approaches in finding the integer solution. The precision-aided PAR (PD-PAR) for attitude determination is explained in the next Section.…”

Canopy Volume Extraction of Citrus reticulate Blanco cv. Shatangju Trees Using UAV Image-Based Point Cloud Deep Learning

“…The acquisition platform is DJI Phantom 4 RTK (Real Time Kinematic) UAV (DJI, Shenzhen, China), which is equipped with a 20-megapixel CMOS (Complementary Metal-Oxide-Semiconductor) sensor. The RTK and six-way vision sensors allow the flight of the UAV safer and more stable [35][36][37]. The Phantom 4 RTK has a maximum take-off weight of 1391 g, and a maximum horizontal flight speed of 14 m/s.…”

A Collaborative RTK Approach to Precise Positioning for Vehicle Swarms in Urban Scenarios

“…Nevertheless, RTK positioning performance can be easily jeopardized for various reasons, e.g., the separation between base and target is excessively large, multipath effects or limited/constrained satellite visibility. Some of the aforementioned challenges may be circumvented in various manners, for instance, long distance baselines can be palliated using partial ambiguity resolution techniques [8], [9], and multipath effects can be hindered with robust estimation [10], [11], beamforming [12], [13] or multi-sensor integration [14], [15]. Unfortunately, some of these solutions are challenging to implement at a mass-market level, due to its high computational and economical cost.…”