Modeling random gyro drift by time series neural networks and by traditional method

This paper presents modeling random gyro drift rate by traditional time series method , and makes compensation for gyro drift by Kalman filter, and proposes the modeling and forecasting method by neural networks for strapdown gyro based on time series analysis, and makes a research for random drift rate of gyro applied for strapdown inertial navigation systems, comparison between the results of by Kalman filter based traditional time series method and by time series neural networks is presented.

[1]  John J. Shynk,et al.  Statistical analysis of the single-layer backpropagation algorithm. I. mean weight behavior , 1993, IEEE Trans. Signal Process..

[2]  Allan Dushman.  On Gyro Drift Models and Their Evaluation , 1962, IRE Transactions on Aerospace and Navigational Electronics.

[3]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[4]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[5]  James V. Hansen,et al.  Neural networks and traditional time series methods: a synergistic combination in state economic forecasts , 1997, IEEE Trans. Neural Networks.

[6]  Sudhakar Paniit,et al.  Modeling Random Gyro Drift Rate by Data Dependent Systems , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[7]  H. J. Sandberg,et al.  Stationary and nonstationary characteristics of gyro drift rate , 1969 .

[8]  Albert J. Van Dierendonc,et al.  Modeling Nonstationary Random Processes with an Application to Gyro Drift Rate , 1969, IEEE Transactions on Aerospace and Electronic Systems.

[9]  John J. Shynk,et al.  Statistical analysis of the single-layer backpropagation algorithm. II. MSE and classification performance , 1993, IEEE Trans. Signal Process..

[10]  Robert L. Hammon.  An Application of Random Process Theory to Gyro Drift Analysis , 1960, IRE Transactions on Aeronautical and Navigational Electronics.