Outlier accommodation for meter-level positioning: Risk-averse performance-specified state estimation

Abstract

Autonomous vehicle operation would be enhanced if a specified level of accuracy could be guaranteed. Risk-averse performance-specified (RAPS) state estimation works within an optimization setting to choose the set of measurements that achieves a performance specification with minimum risk of inclusion of outliers. This paper considers the challenge of preventing outlier measurements from affecting the accuracy and reliability of state estimation, with minimal risk. This paper is that for the first time applies the RAPS approach to the GNSS vehicle state estimation problem, including a review of the theoretical derivation and experimental results. The experimental results utilize real-world Doppler and differential pseudorange data that allows a comparative study with the standard Neyman-Pearson (NP) approach and RAPS state estimation.