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