Abstract
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.
Highlights
Inertial sensors are valuable sensors for navigation of aircraft systems, vehicles and strategic weapons [1]
Sensor and low-precision Fiber Optic Gyro (FOG) were collected. The reason this MEMS sensor was tested in the first experiment is that this sensor exhibits angle random walk (ARW), rate random walk (RRW), and bias instability [6,15]
Because of quantization noise considered in the second experiment, we used the proposed and traditional Allan variance methods to analyze the stochastic errors of FOG
Summary
Inertial sensors are valuable sensors for navigation of aircraft systems, vehicles and strategic weapons [1]. Frequency and time-domain approaches have been used to model the stochastic errors of inertial sensors. As a frequency-domain method, power spectral density (PSD) is commonly used to investigate the stochastic errors of inertial sensors. As a time-domain analysis technique, the Allan variance is a simple and useful method in determining the characteristics of the underlying random processes causing the data noise. It has been widely used for identifying stochastic processes such as quantization noise, white noise, correlated noise, sinusoidal noise, random walk, and flicker noise in inertial sensors [8,9,10]
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