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.

As a noise analysis of MEMS IMU, the traditional Allan variance methods have large computational burden because of requiring to store a large amount of data. Moreover, the procedure of drawing slope lines for estimation is also painful. In order to overcome these drawbacks, a online method is proposed to estimate the Allan variance parameters, which directly model sensors random errors including quantization noise, angular random walk, bias instability, rate random walk and rate ramp into a nonlinear state space model and then implemented by sage-husa adaptive Kalman filter algorithm. The comparison of results of real ADIS16405 IMU static gyro noise analyzed by Allan variance method and the proposed approach shows that the results from the proposed method are well within the error limits of Allan variance method. Moreover, the technique proposed here estimates the Allan variance coefficients in real time, effectively avoids storage of history data and manual analysis for an Allan variance graph

Although angle random walk (ARW) of fiber optic gyroscope (FOG) has been well modeled and identified before being integrated into the high-accuracy attitude control system of satellite, aging and unexpected failures can affect the performance of FOG after launch, resulting in the variation of ARW coefficient. Therefore, the ARW coefficient can be regarded as an indicator of "state of health" for FOG diagnosis in some sense. The Allan variance method can be used to estimate ARW coefficient of FOG, however, it requires a large amount of data to be stored. Moreover, the procedure of drawing slope lines for estimation is painful. To overcome the barriers, a weighted state-space model that directly models the ARW to obtain a nonlinear state-space model was established for FOG. Then, a neural extended-Kalman filter algorithm was implemented to estimate and track the variation of ARW in real time. The results of experiment show that the proposed approach is valid to detect the state of FOG. Moreover, the proposed technique effectively avoids the storage of data.

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To further improve the forecast accuracy of the geometric error of CNC machine tool, a low order Fourier polynomials fitting model (LFPFM) parametric modeling method based on Improved Gray Wolf Optimization(IGWO) algorithm is proposed. Straight-axis geometric error term is analyzed, and the introduction of piecewise attenuation factor can be adjusted to the GWO algorithm is Improved, to improve the scouting speed of the Optimization algorithm and precision, the error data in all parameters based on the IGWO algorithm to find the best fitting values, and get the error about low-order Fourier polynomial fitting polynomial, Use laser interferometer to measure the location errors of three straight line axes in the CNC machine tool, and parametric modeling was carried out. The experimental results show that the machine tool error parameterized model based on the LFPFM the IGWO algorithm can fit the error data well, and the residual error is smaller, the accuracy is higher, the fitting effect is better, so it is more suitable for the prediction and compensation model of CNC machine tool geometric error.