Outlier Accommodation By Risk-Averse Performance-Specified Nonlinear State Estimation: GNSS Aided INS
Farzana Rahman, Elahe Aghapour, Jay A. Farrell · 2018
This paper considers the challenge of preventing outlier measurements from affecting the accuracy and reliability of state estimation. Specifically, the paper extends the Risk-Averse Performance-Specified (RAPS) algorithm to nonlinear systems and applies it the Global Navigation Satellite Systems (GNSS) aiding an Inertial Navigation System (INS). The paper includes an application example, using data from a challenging environment, that allows a comparative study with the standard Neyman-Pearson (NP) test based extended Kalman Filter (EKF). In the experiment different aspects of measurements (e.g. risk metric, GDOP, no. of measurements) are used to highlight the trade-offs between the important task of removing the effect of risky measurements even though this removal decreases the spatial diversity of the remaining measurement set.