Double-layer Cubature Kalman Filter

Yang, Feng; Luo, Yujuan; Zheng, Litao; Chen, Shaodong; Zhang, Jie

doi:10.1109/iccais.2018.8570334

Cited by 1 publication

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper is an extended and revised version of the conference paper [ 33 ]. It provides new simulations, improvements, and further details, including mainly the following: (a) more detailed derivations and discussions about the correlated algorithms are added; (b) the fundamentals of the proposed algorithm is revised to be more comprehensible; (c) the weight of the updated cubature point in the outer CKF of the DLCKF is added as well as the flowsheet of the DLCKF is revised for better formulations; (d) four algorithms (UKF, ICKF, UPF, RUCKF) and applications in real-world scenario are added in the simulation to demonstrate the excellent performance of the DLCKF.…”

Section: Introductionmentioning

confidence: 99%

Double-Layer Cubature Kalman Filter for Nonlinear Estimation

Yang

Luo

Zheng

2019

Sensors

Self Cite

View full text Add to dashboard Cite

The cubature Kalman filter (CKF) has poor performance in strongly nonlinear systems while the cubature particle filter has high computational complexity induced by stochastic sampling. To address these problems, a novel CKF named double-Layer cubature Kalman filter (DLCKF) is proposed. In the proposed DLCKF, the prior distribution is represented by a set of weighted deterministic sampling points, and each deterministic sampling point is updated by the inner CKF. Finally, the update mechanism of the outer CKF is used to obtain the state estimations. Simulation results show that the proposed algorithm has not only high estimation accuracy but also low computational complexity, compared with the state-of-the-art filtering algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%