Miao Zhiyong, Shen Feng, Xu Dingjie, He Kunpeng, Tian Chunmiao
Department of Automation, Harbin Engineering University, Harbin 150000, China.
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150000, China.
Sensors (Basel). 2015 Jan 23;15(2):2496-524. doi: 10.3390/s150202496.
As a noise analysis method for inertial sensors, the traditional Allan variance method requires the storage of a large amount of data and manual analysis for an Allan variance graph. Although the existing online estimation methods avoid the storage of data and the painful procedure of drawing slope lines for estimation, they require complex transformations and even cause errors during the modeling of dynamic Allan variance. To solve these problems, first, a new state-space model that directly models the stochastic errors to obtain a nonlinear state-space model was established for inertial sensors. Then, a neural-extended Kalman filter algorithm was used to estimate the Allan variance coefficients. The real noises of an ADIS16405 IMU and fiber optic gyro-sensors were analyzed by the proposed method and traditional methods. The experimental results show that the proposed method is more suitable to estimate the Allan variance coefficients than the traditional methods. Moreover, the proposed method effectively avoids the storage of data and can be easily implemented using an online processor.
作为一种用于惯性传感器的噪声分析方法，传统的阿伦方差法需要存储大量数据并对阿伦方差图进行人工分析。尽管现有的在线估计方法避免了数据存储以及绘制斜率线进行估计的繁琐过程，但它们需要复杂的变换，甚至在动态阿伦方差建模过程中会导致误差。为了解决这些问题，首先，针对惯性传感器建立了一种直接对随机误差进行建模以获得非线性状态空间模型的新状态空间模型。然后，使用神经扩展卡尔曼滤波算法来估计阿伦方差系数。通过所提方法和传统方法对ADIS16405惯性测量单元（IMU）和光纤陀螺传感器的实际噪声进行了分析。实验结果表明，与传统方法相比，所提方法更适合估计阿伦方差系数。此外，所提方法有效地避免了数据存储，并且可以使用在线处理器轻松实现。
