Huang Lei
Automation Department, Nanjing Forestry University, 159 Longpan Road, Nanjing 210037, China.
Sensors (Basel). 2015 Sep 30;15(10):25277-86. doi: 10.3390/s151025277.
To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required.
为了解决传统的陀螺随机噪声ARMA建模方法需要大量样本且收敛缓慢的问题，开发了一种使用鲁棒卡尔曼滤波的ARMA建模方法。将ARMA模型参数用作状态变量。使用观测噪声的未知时变估计器来实现观测噪声的估计均值和方差。通过鲁棒卡尔曼滤波，可准确估计ARMA模型参数。所开发的ARMA建模方法具有收敛速度快和精度高的优点。因此，所需的样本量减少。它可应用于需要快速准确的ARMA建模方法的陀螺随机噪声建模应用中。
