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

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) bas...

Full description

Bibliographic Details
Main Authors:	Lijun Song, Zhongxing Duan, Bo He, Zhe Li
Format:	Article
Language:	English
Published:	Hindawi-Wiley 2018-01-01
Series:	Complexity
Online Access:	http://dx.doi.org/10.1155/2018/3039061

id	doaj-0dbf2f93fa114adcaa26cb9700a60b32
record_format	Article
spelling	doaj-0dbf2f93fa114adcaa26cb9700a60b322020-11-25T01:01:43ZengHindawi-WileyComplexity1076-27871099-05262018-01-01201810.1155/2018/30390613039061Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer AlignmentLijun Song0Zhongxing Duan1Bo He2Zhe Li3Electronic Information and Control Engineering College, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaElectronic Information and Control Engineering College, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaElectronic Information and Control Engineering College, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaElectronic Information and Control Engineering College, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaThe 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.http://dx.doi.org/10.1155/2018/3039061
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Lijun Song Zhongxing Duan Bo He Zhe Li
spellingShingle	Lijun Song Zhongxing Duan Bo He Zhe Li Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment Complexity
author_facet	Lijun Song Zhongxing Duan Bo He Zhe Li
author_sort	Lijun Song
title	Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment
title_short	Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment
title_full	Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment
title_fullStr	Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment
title_full_unstemmed	Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment
title_sort	application of federal kalman filter with neural networks in the velocity and attitude matching of transfer alignment
publisher	Hindawi-Wiley
series	Complexity
issn	1076-2787 1099-0526
publishDate	2018-01-01
description	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.
url	http://dx.doi.org/10.1155/2018/3039061
work_keys_str_mv	AT lijunsong applicationoffederalkalmanfilterwithneuralnetworksinthevelocityandattitudematchingoftransferalignment AT zhongxingduan applicationoffederalkalmanfilterwithneuralnetworksinthevelocityandattitudematchingoftransferalignment AT bohe applicationoffederalkalmanfilterwithneuralnetworksinthevelocityandattitudematchingoftransferalignment AT zheli applicationoffederalkalmanfilterwithneuralnetworksinthevelocityandattitudematchingoftransferalignment
_version_	1725207833394282496

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

Similar Items