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首页> 外文期刊>Mechanical systems and signal processing >Recurrent-neural-network-based unscented Kalman filter for estimating and compensating the random drift of MEMS gyroscopes in real time
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Recurrent-neural-network-based unscented Kalman filter for estimating and compensating the random drift of MEMS gyroscopes in real time

机译：基于循环的神经网络的无创卡尔曼滤波器，用于估计和补偿实时MEMS陀螺仪的随机漂移

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摘要

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.
机译：MEMS（微电器机械系统）陀螺仪中随机误差的存在使得测量精度具有挑战性的提高。本文解决了一种新的方法来估计和补偿MEMS陀螺仪的随机漂移实时，将Unscented Kalman滤波器（UKF）与复发神经网络（RNN）组合。在所提出的方法中，随机漂移被认为是广义非线性自回归移动平均（NARMA）模型，并且通过动态RNN实现其最佳预测器。为了实时补偿随机漂移，RNN模型被进入UKF的框架，用于建立改进的UKF的状态方程。本文的新颖性是提出了一种策略，以保证UKF和RNN组合的有效性。通过实验验证了所提出的方法的有效性和优越性。

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