<i>Continuous</i> discrete cubature quadrature Kalman filter

Swati, -

doi:10.1002/asjc.2505

Cited by 2 publications

(2 citation statements)

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“…The QCKF algorithm is a Gaussian filter algorithm that estimates the posterior distribution of the probability density function (PDF) of a nonlinear function by utilizing a set of cubature points, thereby circumventing the necessity for linearization of the nonlinear function ( Geng et al, 2021 ; Swati, 2022 ; Wang et al, 2023 ). The concrete implementation procedures of the QCKF algorithm are outlined below:…”

Section: Preliminaries and Problem Formulationmentioning

confidence: 99%

“…The Cubature Kalman filter (CKF) algorithm employs a set of cubature points generated by the third-order sphere-phase diameter cubature rule to approximate the probability density distribution of nonlinear system, which has a more rigorous theoretical basis and better numerical stability than the unscented Kalman Filter algorithm ( Arasaratnam et al, 2010 ; Zhang et al, 2019 ; Chang et al, 2021 ). By combining it with the attitude quaternion method, the obtained quaternion cubature Kalman filter (QCKF) algorithm shows the characteristics of simple implementation, good convergence, high precision, and suitability for high-dimensional systems ( Geng et al, 2021 ; Swati, 2022 ; Wang et al, 2023 ). Furthermore, the quaternion augmented cubature Kalman filter (QACKF) algorithm enhances the estimation accuracy of QCKF algorithm to some extent, but its computational complexity is significantly higher than that of QCKF algorithm ( Wang et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

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A novel Gaussian sum quaternion constrained cubature Kalman filter algorithm for GNSS/SINS integrated attitude determination and positioning system

Dai,

Xiao,

Zhou

et al. 2024

Front. Neurorobot.

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The quaternion cubature Kalman filter (QCKF) algorithm has emerged as a prominent nonlinear filter algorithm and has found extensive applications in the field of GNSS/SINS integrated attitude determination and positioning system (GNSS/SINS-IADPS) data processing for unmanned aerial vehicles (UAV). However, on one hand, the QCKF algorithm is predicated on the assumption that the random model of filter algorithm, which follows a white Gaussian noise distribution. The noise in actual GNSS/SINS-IADPS is not the white Gaussian noise but rather a ubiquitous non-Gaussian noise. On the other hand, the use of quaternions as state variables is bound by normalization constraints. When applied directly in nonlinear non-Gaussian system without considering normalization constraints, the QCKF algorithm may result in a mismatch phenomenon in the filtering random model, potentially resulting in a decline in estimation accuracy. To address this issue, we propose a novel Gaussian sum quaternion constrained cubature Kalman filter (GSQCCKF) algorithm. This algorithm refines the random model of the QCKF by approximating non-Gaussian noise with a Gaussian mixture model. Meanwhile, to account for quaternion normalization in attitude determination, a two-step projection method is employed to constrain the quaternion, which consequently enhances the filtering estimation accuracy. Simulation and experimental analyses demonstrate that the proposed GSQCCKF algorithm significantly improves accuracy and adaptability in GNSS/SINS-IADPS data processing under non-Gaussian noise conditions for Unmanned Aerial Vehicles (UAVs).

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Section: Preliminaries and Problem Formulationmentioning

confidence: 99%