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Application research of multi-source data fusion and multi-model ensemble methods in aircraft approach state prediction | IEEE Journals & Magazine | IEEE Xplore

Application research of multi-source data fusion and multi-model ensemble methods in aircraft approach state prediction


Abstract:

With advances in sensing technology, situational awareness and prediction based on multi-source data have become crucial for ensuring flight safety. Compared to single-mo...Show More

Abstract:

With advances in sensing technology, situational awareness and prediction based on multi-source data have become crucial for ensuring flight safety. Compared to single-model approaches, multi-model ensemble prediction algorithms offer higher accuracy and robustness, yet current ensemble strategies are insufficient for further development. Therefore, this paper introduces a new multi-model ensemble prediction algorithm—the Minimum Consensus Cost Ensemble (MCCE) Prediction Algorithm—specifically for predicting the final approach state of civil aircraft. The MCCE algorithm incorporates the "Minimum Consensus Cost" concept from group decision-making, quantifying the bias of individual models toward different prediction outcomes and dynamically adjusting the ensemble strategy to select the result with the highest consensus among models as the final output. This approach addresses decision bias issues in traditional ensemble models, enhancing stability and accuracy. In experiments, a fusion model was designed to maximize the Silhouette coefficient, combining Convolutional Neural Networks (CNN) and Multi-Head Attention mechanisms to efficiently integrate multi-source data and isolate final approach state features. Through independent modeling and performance comparisons with multiple classification algorithms such as SVM and CNN, results show that the MCCE algorithm achieves approximately 99.97% accuracy, maintaining about 94.71% accuracy even under 20dB SNR noise interference, significantly outperforming other models. Based on MCCE’s consensus cost, the key parameters leading to abnormal approach states in civil aircraft can be identified, helping pilots make timely, effective flight decisions to reduce approach-phase accident risks.
Published in: IEEE Sensors Journal ( Early Access )
Page(s): 1 - 1
Date of Publication: 31 December 2024

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