Paper Info
Title
Outlier Accommodation By Risk-Averse Performance-Specified Nonlinear State Estimation: Gnss Aided Ins
Abstract
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.
Year
DOI
Venue
2018
10.1109/CDC.2018.8618876
2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC)
Field
DocType
ISSN
Inertial navigation system,Data mining,Extended Kalman filter,Antenna diversity,Nonlinear system,Risk metric,Control theory,Computer science,Outlier,GNSS applications,Dilution of precision
Conference
0743-1546
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Farzana Rahman117023.31
Elahe Aghapour201.35
Jay A. Farrell386269.84