A novel IFOG Allan variance coefficients real-time detection method with temperature variation in moorage

Gao, Yanbin; Guan, Lei; Wang, Tingjun; Liu, Shifei

doi:10.1016/j.ijleo.2015.05.110

Cited by 1 publication

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As is well known, state estimation for dynamic systems has been the subject of considerable research interest for several decades and it plays a key role in measurement science and engineering [1][2][3][4][5][6]. Examples include tracking moving targets in multisensor systems [7][8], Kalman filter (KF)-based information fusion between inertial navigation systems and other systems [9][10][11], the online estimation of the Allan variance coefficients of inertial measurement units [12][13][14], the attitude estimation of spacecraft and satellites [15][16][17], and many others. Due to the inherent nonlinearities that exist in many practical problems, the nonlinear filter algorithm is a prominent and widely used technique.…”

Section: Introductionmentioning

confidence: 99%

Neural network-aided variational Bayesian adaptive cubature Kalman filtering for nonlinear state estimation

Zhang

Shi

Zhang

et al. 2017

Meas. Sci. Technol.

View full text Add to dashboard Cite

In this paper, a new variational Bayesian adaptive cubature Kalman filter (VBACKF) is proposed for nonlinear state estimation. Although the conventional VBACKF performs better than cubature Kalman filtering (CKF) in solving nonlinear systems with time-varying measurement noise, its performance may degrade due to the uncertainty of the system model. To overcome this drawback, a multilayer feed-forward neural network (MFNN) is used to aid the conventional VBACKF, generalizing it to attain higher estimation accuracy and robustness. In the proposed neural-network-aided variational Bayesian adaptive cubature Kalman filter (NN-VBACKF), the MFNN is used to turn the state estimation of the VBACKF adaptively, and it is used for both state estimation and in the online training paradigm simultaneously. To evaluate the performance of the proposed method, it is compared with CKF and VBACKF via target tracking problems. The simulation results demonstrate that the estimation accuracy and robustness of the proposed method are better than those of the CKF and VBACKF.

show abstract