卫星信号丢失下航空器多阶段高度预测  

Aircraft Multi-stage Altitude Prediction Under Satellite Signal Loss

作　　者：黄梦婵 苗强 HUANG Mengchan;MIAO Qiang(School of Electrical Engineering,Sichuan University,Chengdu 610065,China)

机构地区：[1]四川大学电气工程学院,四川成都610065

出　　处：《工程科学与技术》2024年第6期44-53,共10页Advanced Engineering Sciences

基　　金：国家自然科学基金项目(52075349;62303335)。

摘　　要：针对卫星信号丢失下航空器高度指示值不准确的问题,提出一种基于注意力机制和时域卷积神经网络的航空器多阶段高度预测算法(LTCA–TCN)。首先,采用模糊逻辑将航空器的整段飞行过程划分为不同阶段,提供多阶段的数据储备。然后,针对航空器飞参长时间序列的特点,设计长时序关联注意力(LTCA)特征提取算法,以提取增强时空关联特征表示;在此基础上,利用时域卷积神经网络(TCN)的时序数据处理能力,构建LTCA–TCN高度预测模型。最后,考虑不同阶段的预测误差容忍度,给出评价模型多阶段高度预测能力的评估指标。利用大气惯导数据集进行实验测试,实验结果表明:LTCA–TCN算法相较于其他对比算法,在多阶段的高度预测中均取得了最优的预测结果,尤其在巡航阶段,本文算法预测结果的均方根误差控制在10 m之内;模拟卫星信号丢失的特定情形,LTCA–TCN算法能够较准确地预测多阶段的惯性卫星组合高度。综上,LTCA–TCN算法具有较高的灵活性与适应性,能够为航空器提供更可靠的导航高度指示值,提升了飞行过程中的安全性与可靠性。Objective Combining an inertial navigation system(INS)and a global positioning system(GPS)in stable conditions of GPS satellite signals offers the most accurate altitude indication,termed inertial satellite composite altitude.When GPS signals are lost or unstable,aircraft must rely only on INS altitude, introducing a discrepancy compared to the composite altitude. This reduction in altitude indication accuracy significantly af-fects navigation performance. Hence, the predictive recovery of aircraft altitude without GPS signals is crucial. Current research faces challenges in mining high-dimensional flight data and enhancing prediction accuracy. This study proposes a multi-stage altitude prediction model using atten-tion mechanisms and temporal convolutional neural networks (TCNs). Methods The aircraft’s flight stages are determined to facilitate targeted altitude prediction for different flight phases. Traditional clustering al-gorithms often struggle to capture transitional states in time-series data. Therefore, a fuzzy logic approach is adopted to map ambiguous inputs to explicit output states, enabling the extraction of climb, cruise, and descent phases from the aircraft’s entire flight process. This segmentation aids in better capturing phase-specific features for the prediction model and provides data reserves for the three stages. Addressing the longitudinal nature of aircraft flight parameter time series, a long temporal correlation attention (LTCA) mechanism is designed for feature extraction, enhan-cing spatiotemporal correlation representation. LTCA efficiently exploited attention mechanisms to extract key features from multi-dimensional flight parameter data samples through adaptive global average pooling (GAP) and one-dimensional convolution, considering global and local in-formation. This approach provided a more effective feature representation for aircraft altitude prediction in the absence of satellite signals. Then, an LTCA-TCN altitude prediction model is constructed using the temporal data

关 键 词：航空器 卫星信号 高度预测 注意力机制 时域卷积神经网络 

分 类 号：V241[航空宇航科学与技术—飞行器设计] V267