Deck motion prediction using neural kernel network Gaussian process regression

被引：0
|
作者：
Qin, Peng [1 ]
Luo, Jianjun [1 ]
Ma, Weihua [1 ]
Wu, Liming [1 ]
机构：
[1] School of Astronautics, Northwestern Polytechnical University, Xi′an
来源：
Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University | 2024年 / 42卷 / 03期
关键词：
automatic carrier landing; automatic composite kernel construction; deck motion prediction; Gaussian process regression; neural kernel networks;
D O I：
10.1051/jnwpu/20244230377
中图分类号：
学科分类号：
摘要：
Deck motion prediction and compensation are critical technologies for carrier-based aircraft automatic landing. Traditional deck motion prediction methods rely on precision of motion models and parameter adjustments, facing challenges in adaptability to complex sea conditions, different types of carriers, changes in flight conditions, and limitations in prediction duration, as well as reliability issues. This paper proposes a deck motion prediction method based on the neural kernel network Gaussian process regression (NKN-GPR) model. The NKN-GPR model can utilize a neural kernel network (NKN) to automatically construct the Gaussian process regression (GPR) model′s composite kernel, effectively addressing the limitations of the automated kernel search (ACKS) algorithm, which heavily depends on manual prior knowledge. Simulation data is generated using a combination of sine wave and power spectrum models, and the NKN-GPR model is compared with an autoregressive (AR) model based on least squares in a simulated validation. The simulation results demonstrate that the NKN-GPR model exhibits significant advantages in motion prediction accuracy, smoothness, and prediction duration, which confirms the effectiveness of the proposed algorithm. This study provides theoretical support for safe automatic landing of carrier-based aircraft. ©2024 Journal of Northwestern Polytechnical University.
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页码：377 / 385
页数：8
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