Application of the Scaled Unscented Kalman Filter with Augmented State in the Fiber Optic Gyroscope

The article presents an approach for combining methods of recursive Bayesian estimation with models of dynamical systems with varying differentiation order. The work addresses the problem of explicit fractional order estimation and tracking by constructing an efficient Unscented Kalman filter, where the model order is directly estimated within an augmented state along with the variables of interest. The feasibility of the estimation method is assessed using a benchmark problem based on a simplified fractional neuron firing rate model and time-dependent differentiation order. The proposed technique is compared to an implicit method based on Interacting Multiple Model filtering and a computationally efficient method using a modification of the Ensemble Kalman filter. The performance with respect to different parameters and filter settings is analyzed and a corresponding discussion is provided.

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A Novel Compensation Method for FOG Temperature Drift Based on TUKF

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.568-570.405 ◽

2014 ◽

Vol 568-570 ◽

pp. 405-410

Author(s):

Yang Li ◽

Bai Qing Hu ◽

Feng Zha ◽

Kai Long Li

Keyword(s):

Fiber Optic ◽

Unscented Kalman Filter ◽

Network Models ◽

Compensation Method ◽

Mean Square ◽

Numerical Instability ◽

Fiber Optic Gyroscope ◽

Elman Network ◽

Temperature Drift ◽

Mean Square Errors

Aiming at the problem of modeling and compensation of the fiber optic gyroscope (FOG) drift caused by temperature, a novel compensation method for FOG temperature drift based on transformed unscented Kalman filter (TUKF) is proposed. Elman network with faster convergence speed is used to modeling and TUKF algorithm is adopted to train the weights of Elman network, which effectively solves the problem of numerical instability. The results prove that the proposed method has higher precision compared with Elman network and IUKF network models. By using the TUKF algorithm, the root mean square errors (RMSE) are improved by 60% in temperature rise period and 50.5% in fall period.

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Estimation of Forcing Function, Mistuning and Modal Damping in a Bladed Rotor

Volume 7A: Structures and Dynamics ◽

10.1115/gt2019-90262 ◽

2019 ◽

Author(s):

Arjun Singh Chauhan ◽

Alok Sinha

Keyword(s):

Kalman Filter ◽

State Space ◽

Unscented Kalman Filter ◽

State Space Model ◽

Space Model ◽

Vibration Data ◽

Forcing Function ◽

Modal Damping ◽

Bladed Rotor ◽

Augmented State

Abstract This paper deals with the estimation of forcing function, modal damping and mistuned modal stiffnesses in a bladed rotor. Previous research on parameter estimation in a mistuned bladed rotor relies on the knowledge of the forcing function as well as the vibration data. This paper presents two novel approaches. The first approach relies on knowledge of both the forcing and vibration data. The parameters are treated as states of the system and an augmented state space model is created. Unscented Kalman Filter is then used on the steady state data to estimate the parameters. The second approach eliminates the dependence on forcing data. Both the forcing and parameters are now treated as states of the system to construct an augmented state space model. Unscented Kalman Filter is then used on transient vibration data for estimation. Numerical results are presented for a simple model of a mistuned bladed rotor which considers a single mode of vibration per blade.

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