Transactions of Nanjing University of Aeronautics & Astronautics
Issue 5,2025 Table of Contents
2025(5):565-576.
DOI: 10.16356/j.1005-1120.2025.05.001
Abstract:
Intelligent interpretation of high-resolution remote sensing imagery is a fundamental challenge in aerospace information processing. Complex ground environments such as construction and demolition （C&D） waste landfills exemplify the need for robust segmentation models that can handle diverse spatial and spectral patterns. Conventional convolutional neural networks （CNNs） are limited by their local receptive fields， whereas Transformer-based architectures often lose fine spatial detail， resulting in incomplete delineation of heterogeneous remote sensing targets. To address these issues， we propose a global-local collaborative network （GLC-Net）， which is designed for intelligent remote sensing image segmentation. The model integrates an efficient Transformer block to capture global dependencies and a local enhancement block to refine structural details. Furthermore， a multi-scale spatial aggregation and enhancement （MSAE） module is introduced to strengthen contextual representation and suppress background noise. Deep supervision facilitates hierarchical feature learning. Experiments on two high-resolution remote sensing datasets （Changping and Daxing） demonstrate that GLC-Net surpasses state-of-the-art baselines by 1.5%—3.2% in mean intersection over union （mIoU）， while achieving superior boundary precision and semantic consistency. These results confirm that global-local collaborative modeling provides an effective pathway for intelligent remote sensing image segmentation in aerospace environmental monitoring.
Intelligent interpretation of high-resolution remote sensing imagery is a fundamental challenge in aerospace information processing. Complex ground environments such as construction and demolition （C&D） waste landfills exemplify the need for robust segmentation models that can handle diverse spatial and spectral patterns. Conventional convolutional neural networks （CNNs） are limited by their local receptive fields， whereas Transformer-based architectures often lose fine spatial detail， resulting in incomplete delineation of heterogeneous remote sensing targets. To address these issues， we propose a global-local collaborative network （GLC-Net）， which is designed for intelligent remote sensing image segmentation. The model integrates an efficient Transformer block to capture global dependencies and a local enhancement block to refine structural details. Furthermore， a multi-scale spatial aggregation and enhancement （MSAE） module is introduced to strengthen contextual representation and suppress background noise. Deep supervision facilitates hierarchical feature learning. Experiments on two high-resolution remote sensing datasets （Changping and Daxing） demonstrate that GLC-Net surpasses state-of-the-art baselines by 1.5%—3.2% in mean intersection over union （mIoU）， while achieving superior boundary precision and semantic consistency. These results confirm that global-local collaborative modeling provides an effective pathway for intelligent remote sensing image segmentation in aerospace environmental monitoring.
2025(5):577-588.
DOI: 10.16356/j.1005-1120.2025.05.002
Abstract:
High-resolution sub-meter satellite data play an increasingly crucial role in the 3D real-scene China construction initiative. Current research on 3D reconstruction using high-resolution satellite data primarily focuses on two approaches： Multi-stereo fusion and multi-view matching. While algorithms based on these two methodologies for multi-view image 3D reconstruction have reached relative maturity， no systematic comparison has been conducted specifically on satellite data to evaluate the relative merits of multi-stereo fusion versus multi-view matching methods. This paper conducts a comparative analysis of the practical accuracy of both approaches using high-resolution satellite datasets from diverse geographical regions. To ensure fairness in accuracy comparison， both methodologies employ non-local dense matching for cost optimization. Results demonstrate that the multi-stereo fusion method outperforms multi-view matching in all evaluation metrics， exhibiting approximately 1.2% higher average matching accuracy and 10.7% superior elevation precision in the experimental datasets. Therefore， for 3D modeling applications using satellite data， we recommend adopting the multi-stereo fusion approach for digital surface model （DSM） product generation.
High-resolution sub-meter satellite data play an increasingly crucial role in the 3D real-scene China construction initiative. Current research on 3D reconstruction using high-resolution satellite data primarily focuses on two approaches： Multi-stereo fusion and multi-view matching. While algorithms based on these two methodologies for multi-view image 3D reconstruction have reached relative maturity， no systematic comparison has been conducted specifically on satellite data to evaluate the relative merits of multi-stereo fusion versus multi-view matching methods. This paper conducts a comparative analysis of the practical accuracy of both approaches using high-resolution satellite datasets from diverse geographical regions. To ensure fairness in accuracy comparison， both methodologies employ non-local dense matching for cost optimization. Results demonstrate that the multi-stereo fusion method outperforms multi-view matching in all evaluation metrics， exhibiting approximately 1.2% higher average matching accuracy and 10.7% superior elevation precision in the experimental datasets. Therefore， for 3D modeling applications using satellite data， we recommend adopting the multi-stereo fusion approach for digital surface model （DSM） product generation.
2025(5):589-600.
DOI: 10.16356/j.1005-1120.2025.05.003
Abstract:
In remote sensing imagery， approximately 67% of the data are affected by cloud cover， significantly increasing the difficulty of image classification， recognition， and other downstream interpretation tasks. To effectively address the randomness of cloud distribution and the non-uniformity of cloud thickness， we propose a coarse-to-fine thin cloud removal architecture based on the observations of the random distribution and uneven thickness of cloud. In the coarse-level declouding network， we innovatively introduce a multi-scale attention mechanism， i.e.， pyramid non-local attention （PNA）. By integrating global context with local detail information， it specifically addresses image quality degradation caused by the uncertainty in cloud distribution. During the fine-level declouding stage， we focus on the impact of cloud thickness on declouding results （primarily manifested as insufficient detail information）. Through a carefully designed residual dense module， we significantly enhance the extraction and utilization of feature details. Thus， our approach precisely restores lost local texture features on top of coarse-level results， achieving a substantial leap in declouding quality. To evaluate the effectiveness of our cloud removal technology and attention mechanism， we conducted comprehensive analyses on publicly available datasets. Results demonstrate that our method achieves state-of-the-art performance across a wide range of techniques.
In remote sensing imagery， approximately 67% of the data are affected by cloud cover， significantly increasing the difficulty of image classification， recognition， and other downstream interpretation tasks. To effectively address the randomness of cloud distribution and the non-uniformity of cloud thickness， we propose a coarse-to-fine thin cloud removal architecture based on the observations of the random distribution and uneven thickness of cloud. In the coarse-level declouding network， we innovatively introduce a multi-scale attention mechanism， i.e.， pyramid non-local attention （PNA）. By integrating global context with local detail information， it specifically addresses image quality degradation caused by the uncertainty in cloud distribution. During the fine-level declouding stage， we focus on the impact of cloud thickness on declouding results （primarily manifested as insufficient detail information）. Through a carefully designed residual dense module， we significantly enhance the extraction and utilization of feature details. Thus， our approach precisely restores lost local texture features on top of coarse-level results， achieving a substantial leap in declouding quality. To evaluate the effectiveness of our cloud removal technology and attention mechanism， we conducted comprehensive analyses on publicly available datasets. Results demonstrate that our method achieves state-of-the-art performance across a wide range of techniques.
2025(5):601-614.
DOI: 10.16356/j.1005-1120.2025.05.004
Abstract:
The advancement of imaging resolution has made the impact of multi-frequency composite jitter in satellite platforms on non-collinear time delay and integration （TDI） charge-coupled device （CCD） imaging systems increasingly critical. Moreover， the accuracy of jitter detection is constrained by the limited inter-chip overlap region inherent to non-collinear TDI CCDs. To address these challenges， a multi-frequency jitter detection method is proposed， achieving sub-pixel level error extraction. Furthermore， a multi-frequency jitter fitting approach utilizing a scale-adjustable sliding window is introduced. For composite multi-frequency jitter， spectral analysis decomposes the relative jitter error curve， while the scale-adjustable sliding window enables frequency-division fitting and modeling. Validation experiments using Gaofen-8 （GF-8） remote sensing satellite imagery detected jitter at 0.65， 20， and 100 Hz in the cross-track direction and at 0.5， 100， and 120 Hz in the along-track direction， demonstrating the method’s precision in detecting platform jitter at sub-pixel accuracy （<0.2 pixels） and its efficacy in fitting and modeling for non-collinear TDI CCD imaging systems subject to multi-frequency jitter.
The advancement of imaging resolution has made the impact of multi-frequency composite jitter in satellite platforms on non-collinear time delay and integration （TDI） charge-coupled device （CCD） imaging systems increasingly critical. Moreover， the accuracy of jitter detection is constrained by the limited inter-chip overlap region inherent to non-collinear TDI CCDs. To address these challenges， a multi-frequency jitter detection method is proposed， achieving sub-pixel level error extraction. Furthermore， a multi-frequency jitter fitting approach utilizing a scale-adjustable sliding window is introduced. For composite multi-frequency jitter， spectral analysis decomposes the relative jitter error curve， while the scale-adjustable sliding window enables frequency-division fitting and modeling. Validation experiments using Gaofen-8 （GF-8） remote sensing satellite imagery detected jitter at 0.65， 20， and 100 Hz in the cross-track direction and at 0.5， 100， and 120 Hz in the along-track direction， demonstrating the method’s precision in detecting platform jitter at sub-pixel accuracy （<0.2 pixels） and its efficacy in fitting and modeling for non-collinear TDI CCD imaging systems subject to multi-frequency jitter.
2025(5):615-628.
DOI: 10.16356/j.1005-1120.2025.05.005
Abstract:
Conventional adaptive filtering algorithms often exhibit performance degradation when processing multipath interference in raw echoes of spaceborne synthetic aperture radar （SAR） systems due to anomalous outliers， manifesting as insufficient convergence and low estimation accuracy. To address this issue， this study proposes a novel robust adaptive filtering algorithm， namely the M-estimation-based minimum error entropy with affine projection （APMMEE） algorithm. This algorithm inherits the joint multi-data-block update mechanism of the affine projection algorithm， enabling rapid adaptation to the dynamic characteristics of raw echoes and achieving fast convergence. Meanwhile， it incorporates the M-estimation-based minimum error entropy （MMEE） criterion， which weights error samples in raw echoes through M-estimation functions， effectively suppressing outlier interference during the algorithm update. Both the system identification simulations and practical multipath interference suppression experiments using raw echoes demonstrate that the proposed APMMEE algorithm exhibits superior filtering performance.
Conventional adaptive filtering algorithms often exhibit performance degradation when processing multipath interference in raw echoes of spaceborne synthetic aperture radar （SAR） systems due to anomalous outliers， manifesting as insufficient convergence and low estimation accuracy. To address this issue， this study proposes a novel robust adaptive filtering algorithm， namely the M-estimation-based minimum error entropy with affine projection （APMMEE） algorithm. This algorithm inherits the joint multi-data-block update mechanism of the affine projection algorithm， enabling rapid adaptation to the dynamic characteristics of raw echoes and achieving fast convergence. Meanwhile， it incorporates the M-estimation-based minimum error entropy （MMEE） criterion， which weights error samples in raw echoes through M-estimation functions， effectively suppressing outlier interference during the algorithm update. Both the system identification simulations and practical multipath interference suppression experiments using raw echoes demonstrate that the proposed APMMEE algorithm exhibits superior filtering performance.
2025(5):629-637.
DOI: 10.16356/j.1005-1120.2025.05.006
Abstract:
A new electrical toothed band brake is proposed based on the planetary gear shifting transmission. The corresponding mathematical model and the finite element model are established to investigate the braking dynamic characteristics and the stress distribution of brake components. According to the structural features and working principle of the brake， the braking process can be divided into a gap elimination stage， a sliding stage， a meshing stage， and a collision stage. The greater the initial speed of brake drum， the higher the impact torque in the collision stage， and the larger the stress of brake components. The ideal range of initial speed is 50—100 r/min， and the ultimate stress is 514 MPa appeared in the right brake band. This study present a wide range of possibilities for further investigation and application of the electrical toothed band brake.
A new electrical toothed band brake is proposed based on the planetary gear shifting transmission. The corresponding mathematical model and the finite element model are established to investigate the braking dynamic characteristics and the stress distribution of brake components. According to the structural features and working principle of the brake， the braking process can be divided into a gap elimination stage， a sliding stage， a meshing stage， and a collision stage. The greater the initial speed of brake drum， the higher the impact torque in the collision stage， and the larger the stress of brake components. The ideal range of initial speed is 50—100 r/min， and the ultimate stress is 514 MPa appeared in the right brake band. This study present a wide range of possibilities for further investigation and application of the electrical toothed band brake.
2025(5):638-647.
DOI: 10.16356/j.1005-1120.2025.05.007
Abstract:
The conventional feedforward hybrid active noise control （FFHANC） system combines the advantages of the feedforward narrowband active noise control （FFNANC） system and the feedforward broadband active noise control （FFBANC） system. To enhance its adaptive adjustment capability under frequency mismatch （FM） conditions， this paper introduces a narrowband frequency adaptive estimation module into the conventional FFHANC system. This module integrates an autoregressive （AR） model and a linear cascaded adaptive notch filter （LCANF）， enabling accurate reference signal frequency estimation even under significant FM. Furthermore， in order to improve the coherence between narrowband and broadband components in the system’s error signal and its corresponding control filter for the conventional FFHANC system， this paper proposes an algorithm based on autoregressive bandpass filter bank （AR-BPFB） for error separation. Simulation results demonstrate that the proposed FFHANC system maintains robust performance under high FM conditions and effectively suppresses hybrid-band noise. The AR-BPFB algorithm significantly elevates the convergence speed of the FFHANC system.
The conventional feedforward hybrid active noise control （FFHANC） system combines the advantages of the feedforward narrowband active noise control （FFNANC） system and the feedforward broadband active noise control （FFBANC） system. To enhance its adaptive adjustment capability under frequency mismatch （FM） conditions， this paper introduces a narrowband frequency adaptive estimation module into the conventional FFHANC system. This module integrates an autoregressive （AR） model and a linear cascaded adaptive notch filter （LCANF）， enabling accurate reference signal frequency estimation even under significant FM. Furthermore， in order to improve the coherence between narrowband and broadband components in the system’s error signal and its corresponding control filter for the conventional FFHANC system， this paper proposes an algorithm based on autoregressive bandpass filter bank （AR-BPFB） for error separation. Simulation results demonstrate that the proposed FFHANC system maintains robust performance under high FM conditions and effectively suppresses hybrid-band noise. The AR-BPFB algorithm significantly elevates the convergence speed of the FFHANC system.
2025(5):648-658.
DOI: 10.16356/j.1005-1120.2025.05.008
Abstract:
In order to explore the opening force variation rules and influencing factors of parafoil opening process， a dynamic model for parafoil opening process is established in this paper. The performance of the parafoil opening process is calculated using the Runge-Kutta method. The calculation results are consistent with the patterns of the existing literatures， showing a maximum opening force error of 4.8%. Based on this， simulations are conducted for 20 different operating conditions of the parafoil system， and the rules governing the changes in system motion speed and parafoil opening force are obtained. The influence of the parafoil parameters and opening conditions on the opening force is also investigated. The results indicate that the opening force is positively correlated with the load mass， the opening speed， and trajectory angle， while it is negatively correlated with the opening height. The peak time of the opening force is affected by aerodynamic force and decelerating inertia force. As the weight and the opening height increase， the system deceleration becomes slower， and the peak time of the opening force is delayed. The aerodynamic force increases with the canopy area and the opening speed， leading to an advancement in the peak time of the opening force. Finally， the Sobol global sensitivity analysis method is employed to obtain the first-order sensitivity and total sensitivity coefficients of the parafoil parameters and opening conditions on parafoil maximum opening force. The results show that the opening speed and the load mass significantly affect the maximum opening force. The first-order sensitivity coefficients of 0.410 7 and 0.313 6， respectively； and the total sensitivity coefficients of 0.477 5 and 0.375 2， respectively. The sensitivity of the canopy area is at a moderate level， with the first-order and total sensitivity coefficients of 0.074 9 and 0.085 1，respectively. The sensitivity coefficients for the opening height and the opening angle are close to zero， indicating that fluctuations in their values have little effect on the maximum opening force.
In order to explore the opening force variation rules and influencing factors of parafoil opening process， a dynamic model for parafoil opening process is established in this paper. The performance of the parafoil opening process is calculated using the Runge-Kutta method. The calculation results are consistent with the patterns of the existing literatures， showing a maximum opening force error of 4.8%. Based on this， simulations are conducted for 20 different operating conditions of the parafoil system， and the rules governing the changes in system motion speed and parafoil opening force are obtained. The influence of the parafoil parameters and opening conditions on the opening force is also investigated. The results indicate that the opening force is positively correlated with the load mass， the opening speed， and trajectory angle， while it is negatively correlated with the opening height. The peak time of the opening force is affected by aerodynamic force and decelerating inertia force. As the weight and the opening height increase， the system deceleration becomes slower， and the peak time of the opening force is delayed. The aerodynamic force increases with the canopy area and the opening speed， leading to an advancement in the peak time of the opening force. Finally， the Sobol global sensitivity analysis method is employed to obtain the first-order sensitivity and total sensitivity coefficients of the parafoil parameters and opening conditions on parafoil maximum opening force. The results show that the opening speed and the load mass significantly affect the maximum opening force. The first-order sensitivity coefficients of 0.410 7 and 0.313 6， respectively； and the total sensitivity coefficients of 0.477 5 and 0.375 2， respectively. The sensitivity of the canopy area is at a moderate level， with the first-order and total sensitivity coefficients of 0.074 9 and 0.085 1，respectively. The sensitivity coefficients for the opening height and the opening angle are close to zero， indicating that fluctuations in their values have little effect on the maximum opening force.
2025(5):659-678.
DOI: 10.16356/j.1005-1120.2025.05.009
Abstract:
During aircraft ground steering， the nose landing gear （NLG） tires of large transport aircraft often experience excessive lateral loads， leading to sideslip. This compromises steering safety and accelerates tire wear. To address this issue， the rear landing gear is typically designed to steer in coordination with the nose wheels， reducing sideslip and improving maneuverability. This study examines how structural parameters and weight distribution affect the performance of coordinated steering in landing gear design for large transport aircraft. Using the C-5 transport aircraft as a case study， we develop a multi-wheel ground steering dynamics model， incorporating the main landing gear （MLG） deflection. A ground handling dynamics model is also established to evaluate the benefits of coordinated steering for rear MLG during steering. Additionally， the study analyzes the impact of structural parameters such as stiffness and damping on the steering performance of the C-5. It further investigates the effects of weight distribution， including the center-of-gravity （CG） height， the longitudinal CG position， and the mass asymmetry. Results show that when the C-5 employs coordinated steering for rear MLG， the lateral friction coefficients of the NLG tires decrease by 22%， 24%， 26%， and 27%. The steering radius is reduced by 29.7%， and the NLG steering moment decreases by 19%， significantly enhancing maneuverability. Therefore， in the design of landing gear for large transport aircraft， coordinated MLG steering， along with optimal structural and CG position parameters， should be primary design objectives. These results provide theoretical guidance for the design of multi-wheel landing gear systems in large transport aircraft.
During aircraft ground steering， the nose landing gear （NLG） tires of large transport aircraft often experience excessive lateral loads， leading to sideslip. This compromises steering safety and accelerates tire wear. To address this issue， the rear landing gear is typically designed to steer in coordination with the nose wheels， reducing sideslip and improving maneuverability. This study examines how structural parameters and weight distribution affect the performance of coordinated steering in landing gear design for large transport aircraft. Using the C-5 transport aircraft as a case study， we develop a multi-wheel ground steering dynamics model， incorporating the main landing gear （MLG） deflection. A ground handling dynamics model is also established to evaluate the benefits of coordinated steering for rear MLG during steering. Additionally， the study analyzes the impact of structural parameters such as stiffness and damping on the steering performance of the C-5. It further investigates the effects of weight distribution， including the center-of-gravity （CG） height， the longitudinal CG position， and the mass asymmetry. Results show that when the C-5 employs coordinated steering for rear MLG， the lateral friction coefficients of the NLG tires decrease by 22%， 24%， 26%， and 27%. The steering radius is reduced by 29.7%， and the NLG steering moment decreases by 19%， significantly enhancing maneuverability. Therefore， in the design of landing gear for large transport aircraft， coordinated MLG steering， along with optimal structural and CG position parameters， should be primary design objectives. These results provide theoretical guidance for the design of multi-wheel landing gear systems in large transport aircraft.
2025(5):679-692.
DOI: 10.16356/j.1005-1120.2025.05.010
Abstract:
The icing of areo-engine inlet components during flight can affect engine operational safety. Conventional hot-air anti-icing systems require a large amount of bleed air， which compromises engine performance. Consequently， low-energy anti/de-icing methods based on superhydrophobic surfaces have attracted widespread attention. Previous studies have demonstrated that for stationary components， superhydrophobic surfaces can significantly reduce anti-icing energy consumption by altering the flow behavior of runback water. However， for rotating inlet components of aero-engines， the effectiveness of superhydrophobic surfaces and the influence of surface wettability on the evolution of runback water flow remain unclear due to the effects of centrifugal and Coriolis forces. This study establishes a 3D liquid water flow simulation model using the volume of fluid （VOF） method to investigate the effects of rotational speed， airflow velocity， and surface wettability on the runback water flow behavior over the rotating spinner under dynamic rotation conditions. The results show that the rotational effects and surface wettability mutually reinforce one another. Specifically， increasing the rotational speed and contact angle can both enhance the flow velocity of liquid water and accelerate the breakup and rupture of liquid film， leading to the formation of rivulets， droplets， and subsequent detachment from the surface. A theoretical model based on force balance is proposed to describe the evolution of runback water flow， and the analysis reveals that as the rotational speed and contact angle increase， the water film is more likely to break up to form rivulets and beads， and the critical radius for droplet detachment from the surface decreases， making it easier removal from the surface.
The icing of areo-engine inlet components during flight can affect engine operational safety. Conventional hot-air anti-icing systems require a large amount of bleed air， which compromises engine performance. Consequently， low-energy anti/de-icing methods based on superhydrophobic surfaces have attracted widespread attention. Previous studies have demonstrated that for stationary components， superhydrophobic surfaces can significantly reduce anti-icing energy consumption by altering the flow behavior of runback water. However， for rotating inlet components of aero-engines， the effectiveness of superhydrophobic surfaces and the influence of surface wettability on the evolution of runback water flow remain unclear due to the effects of centrifugal and Coriolis forces. This study establishes a 3D liquid water flow simulation model using the volume of fluid （VOF） method to investigate the effects of rotational speed， airflow velocity， and surface wettability on the runback water flow behavior over the rotating spinner under dynamic rotation conditions. The results show that the rotational effects and surface wettability mutually reinforce one another. Specifically， increasing the rotational speed and contact angle can both enhance the flow velocity of liquid water and accelerate the breakup and rupture of liquid film， leading to the formation of rivulets， droplets， and subsequent detachment from the surface. A theoretical model based on force balance is proposed to describe the evolution of runback water flow， and the analysis reveals that as the rotational speed and contact angle increase， the water film is more likely to break up to form rivulets and beads， and the critical radius for droplet detachment from the surface decreases， making it easier removal from the surface.
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