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In this note, we have revisited the previously published paper Particle Filtering for Attitude Estimation Using a Minimal Local-Error Representation. In the revisit, we point out that the quaternion particle filtering based on the local/global representation structure has not made full use of the advantage of the particle filtering in terms of accuracy and robustness. Moreover, a normalized quaternion determining procedure based on the minimum mean-square error approach has been investigated into the quaternion-based particle filtering to obtain the fiducial quaternion for the transformation between quaternion and modified Rodrigues parameter. The modification investigated in this note is expected to make the quaternion particle filtering based on the local/global representation structure more strict.
Inertial measurement units are widely used in different fields to estimate the attitude. Many algorithms have been proposed to improve estimation performance. However, most of them still suffer from 1) inaccurate initial estimation, 2) inaccurate ini
In this note, the attitude and inertial sensors drift biases estimation for Strapdown inertial navigation system is investigated. A semi-analytic method is proposed, which contains two interlaced solution procedures. Specifically, the attitude encodi
This paper proposes a learning method for denoising gyroscopes of Inertial Measurement Units (IMUs) using ground truth data, and estimating in real time the orientation (attitude) of a robot in dead reckoning. The obtained algorithm outperforms the s
In this article we introduce the use of recently developed min/max-plus techniques in order to solve the optimal attitude estimation problem in filtering for nonlinear systems on the special orthogonal (SO(3)) group. This work helps obtain computatio
We consider linear network error correction (LNEC) coding when errors may occur on edges of a communication network of which the topology is known. In this paper, we first revisit and explore the framework of LNEC coding, and then unify two well-know