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

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 processing capability of TCNs. Finally, due to the inability of classical regression model performance metrics to account for error tolerance across different flight phases, a novel evaluation metric called “Score” is proposed to assess the multi-stage altitude prediction capability of the model. This metric considered overestimation and underestima-tion scenarios compared to ground truth, setting error upper and lower bounds to penalize data points exceeding error tolerance limits. The Score aggregates the prediction scores of each data sample for each stage, yielding an overall score and providing a comprehensive evaluation of the model’s performance across flight phases. Results and Discussions This study conducts a series of experiments on a dataset of atmospheric inertial navigation data for fixed-wing aircraft to predict the inertial satellite composite altitude in the event of satellite signal loss, using inertial pressure altitude as the reference baseline. Several comparative experiments are designed to comprehensively evaluate the LTCA-TCN altitude prediction algorithm proposed herein. Three subsets of data corresponding to different flight phases are utilized for altitude prediction, and the performance is compared to commonly used convolu-tional neural networks (CNN), recurrent neural networks (RNN), long short-term memory networks (LSTM), gated recurrent units (GRU), TCN, and the LTCA-TCN algorithm proposed in this study. Experimental results showed that the LTCA-TCN algorithm outperforms other comparat-ive algorithms in root mean square error (RMSE) and average Score metrics. It achieves the best RMSE and Score across all three phases. Com-pared to RNNs and their variants, the LTCA-TCN algorithm yields superior prediction results while maintaining a simpler structure and requiring fewer computational resources. In addition, compared to the baseline TCN algorithm, the proposed LTCA-TCN algorithm reduces RMSE by 1.43 m and lowers Score by 0.08. Particularly in the cruise phase, the RMSE reaches 7.97 m, within a 10 m range, demonstrating high prediction accur-acy. Therefore, the LTCA-TCN algorithm indicates significant advantages in multi-stage altitude prediction tasks. Specific GPS satellite signal loss scenarios are simulated to evaluate the LTCA-TCN algorithm’s performance. Corresponding periods of satellite signal loss are set for the as-cent, cruise, and descent phases to predict the Inertial Satellite Composite Altitude during these periods. Experimental results indicated that the LTCA-TCN model exhibits good fitting capability, effectively capturing different change modalities with high flexibility and adaptability in each phase. A relative error ratio is calculated to measure the extent to which the predicted altitude is closer to the actual value of the inertial satellite composite altitude compared to the inertial pressure altitude provided by INS. The relative error ratios of the LTCA-TCN model for all three phases are within 1, indicating that the model’s predictions are generally more accurate than the inertial pressure altitude. Conclusions The results indicated that the LTCA-TCN model achieves high prediction accuracy across multiple stages, outperforming com-monly used neural network algorithms in the field and providing optimal predictive performance. This model can offer more reliable multi-stage altitude indications for aircraft in the event of satellite signal loss.