Application of federal Kalman filter with neural networks in the velocity and attitude matching of transfer alignment
type: Item
instance of: scholarly article
title: Application of federal Kalman filter with neural networks in the velocity and attitude matching of transfer alignment
zbMATH Open document ID: 1390.93806
DOI: 10.1155/2018/3039061
author: Lijun Song
author: Zhongxing Duan
author: Bo He
author: Zhe Li
published in: Complexity
publication date: 2018-06-25T00:00:00Z
review text: Summary: 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 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.
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 92B20
zbMATH DE Number: 6893955
zbMATH Keywords: federal Kalman filter
zbMATH Keywords: neural networks
zbMATH Keywords: transfer alignment
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/3039061
OpenAlex ID: W2787035910
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description: scientific article; zbMATH DE number 6893955