A Discrete Quaternion Particle Filter Based on Deterministic Sampling for IMU Attitude Estimation

A discrete quaternion filtering algorithm following high-density uncertainty matching-based deterministic sampling scheme is proposed for IMU attitude estimation. Existing quaternion filters rely on the specific distributions directly or the on-tangent-plane Gaussian distribution indirectly to model...

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Bibliographic Details
Published in:IEEE sensors journal 2021-10, Vol.21 (20), p.23266-23277
Main Authors: Zhou, Zhaihe, Zeng, Chuanwei, Tian, Xiangrui, Zeng, Qingxi, Yao, Rui
Format: Article
Language:English
Subjects:
Accuracy
Attitudes
Computing costs
Density
deterministic sampling
Estimation
Filtration
Genetic algorithms
IMU attitude estimation
Manifolds
nonlinear filtering
Normal distribution
particle filter
Particle filters
Position measurement
quaternion
Quaternions
Random sampling
Sensors
Uncertainty
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Summary:A discrete quaternion filtering algorithm following high-density uncertainty matching-based deterministic sampling scheme is proposed for IMU attitude estimation. Existing quaternion filters rely on the specific distributions directly or the on-tangent-plane Gaussian distribution indirectly to model the underlying uncertainty. The key idea of this paper is to apply a Dirac mixture at few deterministic weighted samples for non-parametric interpreting the high-density uncertainty of unit quaternion manifold. In contrast to standard Monte Carlo techniques based on random sampling, the proposed approach is deterministic. Furthermore, the deterministic quaternion particles are obtained by projecting on-sphere uniform points to the unit quaternion manifold via an exponential map. The on-manifold quaternions are then exploited in a discrete Bayesian filtering procedure. We compare the new filter with the genetic algorithm embedded quaternion particle filter (GA-QPF) and the unscented quaternion estimator (USQUE) for accuracy and computation cost by using simulated and practical IMU data. The results illustrate that the proposed estimator possesses lower computational cost than the GA-QPF while providing a comparable accuracy, higher accuracy than the USQUE, as well as more effective against large initialization error.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2021.3109156