Reliable GNSS Joint Position and Attitude Estimation in Harsh Environments through Robust Statistics

Abstract

Next-generation navigation systems require precise and robust solutions, providing information about both the system position and its attitude, of particular interest in intelligent transportation systems and robotics applications. Within this context, Global Navigation Satellite Systems (GNSS) are the main source of positioning data and, in multiple antenna setups, can also provide attitude information. Notice that the use of phase observables is mandatory to obtain a precise solution. In this contribution, we leverage the recently introduced recursive GNSS joint position and attitude (JPA) estimation framework, which has been shown to provide good performance under nominal conditions. The main goal is to further elaborate the JPA problem and to propose a new robust filtering solution able to mitigate the impact of possible outliers, which may otherwise cause a performance breakdown of standard JPA, because of the sensitivity of carrier phase measurements. Illustrative results are provided to support the discussion and show the performance improvement of the proposed approach.