Novel Time Series Modeling Methods for Gyro Random Noise Used in Internet of Things

In the micro-strapdown inertial navigation systems of Internet of Things, modeling and filtering of gyro random noise are a useful approach to reducing sensor error and enhancing navigation accuracy. Time series is a popular choice for the gyro random noise modeling process. This paper contains two parts to research the modeling methods of gyro random noise. In the first part, for the problem that conventional autoregressive (AR) modeling methods for fiber optic gyro random noise require mass data due to the slow convergence speed of parameter estimation, furthermore, these methods do not perform well if there are too few samples (e.g., less than 3000), and a novel AR modeling method based on the adaptive Kalman filter is proposed. The proposed method can work with smaller sample sizes due to its rapid parameter estimation convergence speed; its algorithm also yields very precise results. These advantages make it well-suited to applications that require a fast AR modeling method for gyro random noise. In the second part, an experiment reveals that the random noise characteristic of some micro-electromechanical systems gyros is adapted to the moving average (MA) model. However, from the available literature, research on a gyro random noise modeling method based on an MA model is very rare. Therefore, we discuss and analyze two MA modeling methods for gyro random noise and give the comparisons between their modeling results.