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Complexity
Volume 2018, Article ID 3039061, 7 pages
https://doi.org/10.1155/2018/3039061
Research Article

Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Electronic Information and Control Engineering College, Xi’an University of Architecture and Technology, Xi’an 710055, China

Correspondence should be addressed to Zhongxing Duan; moc.361@naud_xhz

Received 8 July 2017; Accepted 8 October 2017; Published 21 January 2018

Academic Editor: Junpei Zhong

Copyright © 2018 Lijun Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.