Robust Linearly Constrained Filtering for GNSS Position and Attitude Estimation under Antenna Baseline Mismatch

Abstract

Precise navigation solutions are fundamental for new intelligent transportation systems and robotics applications, where attitude also plays an important role. Among the different technologies available, Global Navigation Satellite Systems (GNSS) are the main source of positioning data. In the GNSS context, carrier phase observations are mandatory to obtain precise positioning, and multiple antenna setups must be considered for attitude determination. Position and attitude estimation have been traditionally tackled in a separate manner within the GNSS community, but a recently introduced recursive joint position and attitude (JPA) Kalman filter-like approach has shown the potential benefits of the joint estimation. One of the drawbacks of the original JPA is the assumption of perfect system knowledge, and in particular the baseline distance between antennas, which may not be the case in real-life applications and can lead to a severe performance degradation. The goal of this contribution is to propose a robust filtering approach able to mitigate the impact of a possible GNSS antenna baseline mismatch, exploiting the use of linear constraints. Illustrative results are provided to support the discussion and show the performance improvement, for both GNSS-based attitude-only and JPA estimation.