Precision-Aided Partial Ambiguity Resolution Scheme for Instantaneous RTK Positioning
2021
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
Cramer–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.
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