Transactions of Nanjing University of Aeronautics & Astronautics
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Abstract:
Low-altitude traffic management requires timely inference of both unmanned aerial vehicle （UAV） platform type and likely mission intent. Declared flight plans can provide intent information for cooperative operations， but non-cooperative， abnormal or partially observed UAVs require inference from observable evidence. The existing surveillance methods mainly detect or track UAVs， classify platform type， or predict trajectories. They therefore remain limited when different missions share similar short-term motion and appearance alone lacks temporal context. This study introduces MCFNet， a multimodal framework that combines an external-view RGB image with an 8 s trajectory history for mission-level UAV intent recognition. Its bidirectional cross-attention fusion links local visual regions with trajectory time steps before classification， allowing cues such as payload state or spray effects to be interpreted with the relevant maneuver segments. Modality dropout reduces single-modality shortcuts， and a consistency loss penalizes invalid type-intent pairs. On 7 290 simulated samples covering three UAV types and 15 intents， MCFNet achieves 89.63%± ![]()
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Low-altitude traffic management requires timely inference of both unmanned aerial vehicle （UAV） platform type and likely mission intent. Declared flight plans can provide intent information for cooperative operations， but non-cooperative， abnormal or partially observed UAVs require inference from observable evidence. The existing surveillance methods mainly detect or track UAVs， classify platform type， or predict trajectories. They therefore remain limited when different missions share similar short-term motion and appearance alone lacks temporal context. This study introduces MCFNet， a multimodal framework that combines an external-view RGB image with an 8 s trajectory history for mission-level UAV intent recognition. Its bidirectional cross-attention fusion links local visual regions with trajectory time steps before classification， allowing cues such as payload state or spray effects to be interpreted with the relevant maneuver segments. Modality dropout reduces single-modality shortcuts， and a consistency loss penalizes invalid type-intent pairs. On 7 290 simulated samples covering three UAV types and 15 intents， MCFNet achieves 89.63%
Abstract:
To address the high computational costs of traditional computational fluid dynamics （CFD） in aerodynamic optimization and the limitations of conventional deep learning models， such as slow convergence and insufficient accuracy in capturing complex flow physics， a novel framework integrating convolutional neural network （CNN） and Chebyshev-Kolmogorov-Arnold network （Cheby-KAN） is proposed for 2D airfoil pressure distribution prediction. The framework employs a CNN-based geometry encoder to automatically extract high-dimensional features from signed distance field （SDF） and map them into low-dimensional latent vectors， thereby bypassing traditional manual parameterization. In the regression module， shifted Chebyshev polynomials are introduced as basis functions within the KAN architecture （Cheby-KAN）. Leveraging their orthogonality and superior approximation properties， Cheby-KAN effectively mitigates the Runge phenomenon and enhances numerical stability when resolving high-gradient pressure patterns. Experimental results on a dataset comprising 89 million data points demonstrate that Cheby-KAN achieves a 60% reduction in training time compared to conventional multi-layer perceptron （MLP）， with a 92% decrease in training mean square error （MSE）. Furthermore， the model exhibits exceptional generalization in “zero-shot” tests on unseen airfoils， maintaining a mean absolute error （MAE） of2.1 × 10 - 3 ![]()
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To address the high computational costs of traditional computational fluid dynamics （CFD） in aerodynamic optimization and the limitations of conventional deep learning models， such as slow convergence and insufficient accuracy in capturing complex flow physics， a novel framework integrating convolutional neural network （CNN） and Chebyshev-Kolmogorov-Arnold network （Cheby-KAN） is proposed for 2D airfoil pressure distribution prediction. The framework employs a CNN-based geometry encoder to automatically extract high-dimensional features from signed distance field （SDF） and map them into low-dimensional latent vectors， thereby bypassing traditional manual parameterization. In the regression module， shifted Chebyshev polynomials are introduced as basis functions within the KAN architecture （Cheby-KAN）. Leveraging their orthogonality and superior approximation properties， Cheby-KAN effectively mitigates the Runge phenomenon and enhances numerical stability when resolving high-gradient pressure patterns. Experimental results on a dataset comprising 89 million data points demonstrate that Cheby-KAN achieves a 60% reduction in training time compared to conventional multi-layer perceptron （MLP）， with a 92% decrease in training mean square error （MSE）. Furthermore， the model exhibits exceptional generalization in “zero-shot” tests on unseen airfoils， maintaining a mean absolute error （MAE） of
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