Deck motion prediction using neural kernel network Gaussian process regression

基于神经核网络高斯过程回归的甲板运动预测

School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China

Received: 10 June 2023

Abstract

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.

摘要

甲板运动预测与补偿是舰载机自动着舰的关键技术之一。传统甲板运动预测方法依赖于运动建模的准确性和参数调整，面临复杂海况、不同舰型、航态变化时具有适应性差、预测时长短、结果可靠性低等问题。提出一种基于神经核网络高斯过程回归(NKN-GPR)的甲板运动预测模型，使用神经核网络(NKN)实现高斯过程回归(GPR)模型自动复合核构造，有效改善基于规则库自动核搜索(ACKS)算法依赖人工先验知识的不足。以正弦波组合模型和功率谱模型构造仿真数据，对NKN-GPR模型和基于最小二乘法的自回归(AR)模型进行对比仿真验证，仿真结果表明，NKN-GPR模型在运动预测精度、平滑性、预测时长等方面具有显著优势，证明了所提算法的有效性，可为舰载机自动安全着舰提供理论支撑。