Application of Federated Kalman Filter with neural networks in the velocity and attitude matching of Transfer Alignment
- Release time:2024-08-09
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Affiliation of Author(s):
信息与控制工程学院
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Journal:
Complexity
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Abstract:
The centralized Kalman filter is always applicated 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 are used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two sub-filters, the federal filter is used to fuse the information of the two sub-filters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better to estimate 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.
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First Author:
lizhe,hebo,duanzhongxing,songlijun
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Indexed by:
Journal paper
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Volume:
Article ID 3039061
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Translation or Not:
no
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Date of Publication:
2018-04-18
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