Observation-Differenced Point Mass Filter in Gravity-Aided Inertial Navigation

Wang, Bo; Cai, Tijing; Fang, Ke

doi:10.1109/tmech.2023.3304197

Cited by 2 publications

(1 citation statement)

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“…The Strapdown Inertial Navigation System (SINS) is an autonomous navigation system independent of external information, and is one of the primary navigation means for aircrafts, vessels, cars, missiles, unmanned vehicles, and smart wearable devices [ 1 , 2 ]. However, its navigation errors tend to diverge over time, necessitating integration with other navigation systems such as the Global Navigation Satellite System (GNSS), Odometer and the Doppler Velocity Log (DVL) using the Kalman Filter (KF) [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

The Kinematic Models of the SINS and Its Errors on the SE(3) Group in the Earth-Centered Inertial Coordinate System

Fang,

Cai,

Wang

2024

Sensors

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In this paper, the kinematic models of the Strapdown Inertial Navigation System (SINS) and its errors on the SE(3) group in the Earth-Centered Inertial frame (ECI) are established. On the one hand, with the ECI frame being regarded as the reference, based on the joint representation of attitude and velocity on the SE(3) group, the dynamic of the local geographic coordinate system (n-frame) and the body coordinate system (b-frame) evolve on the differentiable manifold, respectively, and the high-order expansion of the Baker–Campbell–Haussdorff equation compensates for the non-commutative motion errors stimulated by strong maneuverability. On the other hand, the kinematics of the left- and right-invariant errors of the n-frame and the b-frame on the SE(3) group are separately derived, where the errors of the b-frame completely depend on inertial sensor errors, while the errors of the n-frame rely on position errors and velocity errors. In this way, the errors brought by the inconsistency of the reference coordinate system are tackled, and a novel attitude error definition is introduced to separate and decouple the factors affecting the dynamic of the n-frame errors and the b-frame errors for better attitude estimation. Through a turntable experiment and a car-mounted field experiment, the effectiveness of the proposed kinematic models in estimating attitude has been verified, with a remarkable improvement in yaw angle accuracy in the case of large initial misalignment angles, and the models developed have better robustness compared to the traditional SE(3) group-based model.

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Section: Introductionmentioning

confidence: 99%