Title: Outlier Accommodation for Meter-Level Positioning: Risk-Averse Performance-Specified State Estimation
Author(s): Elahe Aghapour, Farzana Rahman, Jay A. Farrell
Published in: Proceedings of IEEE/ION PLANS 2018
April 23 - 26, 2018
Hyatt Regency Hotel
Monterey, CA
Pages: 627 - 633
Cite this article: Aghapour, Elahe, Rahman, Farzana, Farrell, Jay A., "Outlier Accommodation for Meter-Level Positioning: Risk-Averse Performance-Specified State Estimation," Proceedings of IEEE/ION PLANS 2018, Monterey, CA, April 2018, pp. 627-633.
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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 NeymanPearson (NP) approach and RAPS state estimation.