Title :
Modeling random gyro drift by time series neural networks and by traditional method
Author_Institution :
Dept. of Instrum. Sci. & Eng., Southeast Univ., Nanjing, China
Abstract :
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.
Keywords :
Kalman filters; inertial navigation; neural nets; time series; Kalman filter; neural networks; random gyro drift rate; strapdown inertial navigation systems; traditional time series method; Autocorrelation; Cities and towns; Error analysis; Inertial navigation; Instruments; Mathematical model; Neural networks; Predictive models; Testing; Time series analysis;
Conference_Titel :
Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
0-7803-7702-8
DOI :
10.1109/ICNNSP.2003.1279399