Non-redundant high-integrity position estimation robust to sensor bias jumps using MGLR

This paper presents and experimentally evaluates an algorithm named Multiple Generalized Likelihood Ratio (MGLR) for detecting and estimating multiple consecutive measurement biases appearing frequently, in the case of non-redundant sensors; typically the case for a small drone or remotely piloted a...

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Bibliographic Details
Published inDrone systems and applications Vol. 10; no. 1; pp. 343 - 366
Main Authors Öman Lundin, Gustav, Mouyon, Philippe, Manecy, Augustin, Hendeby, Gustaf
Format Journal Article
LanguageEnglish
Published NRC Research Press 2022
Canadian Science Publishing
Subjects
Algorithms
Artificial satellites in navigation
Computer Science
Engineering Sciences
estimation de position
Estimation theory
GLR
integrity
Mathematics
non-redundant measurements
Physics
position estimation
rapport de vraisemblance généralisé (« GLR »)
Sensors
France
non-redundant measurements
integrity
position estimation
GLR
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Summary:This paper presents and experimentally evaluates an algorithm named Multiple Generalized Likelihood Ratio (MGLR) for detecting and estimating multiple consecutive measurement biases appearing frequently, in the case of non-redundant sensors; typically the case for a small drone or remotely piloted aerial vehicle. The algorithm itself is based on the Generalized Likelihood Ratio (GLR) algorithm by Willsky for bias detection and estimation, and introduces additional steps for continuously estimating, compensating, and eliminating measurement biases after detection. An experimental campaign using a car-mounted IMU and GNSS receiver in an urban environment shows the effectiveness of the approach to increase accuracy, consistency, and integrity of the estimate in non-redundant estimation with position measurements subject to time-varying bias.
ISSN:2564-4939
2564-4939
DOI:10.1139/dsa-2022-0010