Recurrent-neural-network-based unscented Kalman filter for estimating and compensating the random drift of MEMS gyroscopes in real time. (15th January 2021)

• Record Type: Journal Article
• Authors: Li, Dinghua; Zhou, Jun; Liu, Yingying
• Is Part Of: Mechanical systems and signal processing. Volume 147(2021)
• Publication Date: 2021-01-15
• Subjects: MEMS gyroscope -- Random drift -- Nonlinear autoregressive moving average model -- Recurrent neural network -- Unscented Kalman filter; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2020.107057
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A generalized nonlinear model of the random drift is built by a dynamic RNN. The RNN model is combined with UKF to filter the random drift in real time. Experiments are carried out to verify the effectiveness of the novel algorithm. Abstract: The presence of the stochastic errors in MEMS (Micro Electro Mechanical Systems) gyroscopes makes the improvement of the measurement precision challenging. This paper addresses a novel method to estimate and compensate the random drift of MEMS gyroscopes in real time, combining unscented Kalman filter (UKF) with recurrent neural network (RNN). In the proposed method, the random drift is regarded as a generalized nonlinear autoregressive moving average (NARMA) model, and its optimal predictor is realized by a dynamic RNN. To compensate the random drift in real time, the RNN model is brought into the framework of UKF, for establishing the state equation of the improved UKF. The novelty of this paper is that a strategy is presented to guarantee the validity of the combination of UKF and RNN. The effectiveness and superiorities of the proposed method are verified by experiments.