Recurrent-neural-network-based unscented Kalman filter for estimating and compensating the random drift of MEMS gyroscopes in real time

Dinghua Li, Jun Zhou, Yingying Liu

Research output: Contribution to journalArticlepeer-review

60 Scopus citations

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.

Original languageEnglish
Article number107057
JournalMechanical Systems and Signal Processing
Volume147
DOIs
StatePublished - 15 Jan 2021

Keywords

  • MEMS gyroscope
  • Nonlinear autoregressive moving average model
  • Random drift
  • Recurrent neural network
  • Unscented Kalman filter

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