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

Abstract

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.