Volume 27,2026 Issue 1

• Contents

  Abstract:2026年27卷第1期目次

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Military Aviation
• A Complex Flight Action Recognition Method Based On the FD_Net Network Recognition Model
  MA Jinlong1 2;LI Zhengxin1;SHI Meilin3;SHAN Shengzhe2;DENG Tao2;WU Shihui1

  Abstract:Because of the problems that accuracy is low in recognizing complex flight action, and in order to enhance the accuracy and reliability of flight parameter data analysis, this paper proposes a flight action recognition method based on time convolutional network mapping anchor boxes. The method is to construct a FD̠Net recognition model by improving the YOLOv3 network structure, transforming the recognition of complex flight action into a problem of regional division and classification in the time di mension. A selection method of complex flight action with key feature parameters is proposed, and an input system with 25 feature parameters is constructed. A data augmentation method based on scale scaling is adopted by solving the problem of sample imbalance. Loss functions for prediction box regression, confidence regression, and classification regression are designed to complete model training. The experi mental results show that compared with the existing methods, the proposed method significantly im proves the accuracy of complex flight action recognition and significantly enhances computational efficien cy, and the effectiveness and practicality of the method are verified.

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• Research on High-Density Drone Conflict Detection Algorithm and Airspace Capacity Assessment
  ZHOU Zhichong1;2;3;ZHAO Guhao1;2 ;WU Yarong1;2 ;YANG Jiale1;2 ;NIU Xuehan2;HUANG Zishuo1

  Abstract:Aimed at the problems that detection efficiency is low, and accuracy of conflict detection is not high among a large number of drones in high-density local battlefield airspace in the future, a real-time and rapid detection of conflicts is obtained among a large number of drones in airspace by constructing a drone protection zone model and a battlefield airspace grid model to extract the airspace position matrix for each drone, and by using the Hadamard product calculation method and the properties of composite and prime factor decomposition for drone conflict detection to identify the drone numbers, positions, altitudes, and other information of conflicts. And on the basis of this, operational layers, avoidance layers and collision thresholds are introduced to establish a quantifiable airspace-capacity evaluation framework. The simula tion results indicate that compared to the traditional methods, this Hadamard product calculation method enables the complexity of conflict detection to reduce from O(Cn-2 n ) to level O(n-1), and the conflict de tection time for 800 drones is controlled within 40 ms, greatly improving the efficiency of conflict detec tion. Through further processing of the conflict set under an acceptable collision probability criterion, reli able airspace-capacity values can be worked out, offering theoretical and technical support for the safe and efficient operation of large-scale, high-density drones in local airspace.

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• An Algorithm of Segmenting Lightweight Drone Image Semanteme Based on Improved PP-LiteSeg
  LI Hao1;HE Yuntao1;LI Zihao2

  Abstract:In response to the problems that segmentation is low in accuracy and detection is slow at speed in detecting drone aerial images in key areas by using existing semantic segmentation algorithm, an im proved PP-LiteSeg lightweight drone image semantic segmentation algorithm is proposed. The algorithm, first, is to design a composite attention fusion module in which the parameter free attention mechanism Si mAM is introduced into the unified attention fusion module to enhance global contextual information and improve the information richness of output features. Afterwards, the model parameters are reduced through replacing the calculation method of convolution in the backbone network from ordinary convolu tion to a combination of partial convolution and small-scale convolution kernels. At the same time, a new backbone network SDTCM_PNet is designed to further enhance the lightweighting of the model by chan ging the feature concatenation method of short-term dense connection modules in the multi-layer receptive field of the backbone network. The experimental results conducted on the self-collected drone aerial image dataset show that the algorithm proposed in this paper is valid. Simultaneously, the algorithm is to be de ployed and tested on embedded devices, and the algorithm also meets the needs of real-time.

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• Research on Simulation for Short-Circuit Faults and Residual Conversion in Small Multi-Electric UAVs
  WANG Yuhe;KONG De;JIANG Wen;JIANG Nan;SHAO Haibin

  Abstract:In order to reduce the risk of destructive physical tests such as short circuits, a corresponding short-circuit fault protection and power supply redundancy conversion mechanism is designed in the light of a certain of small aircraft power supply architecture. Based on AMESim, a complete power system and backend load model are established, and protection logic is injected to simulate actual faults and predict phenomena. The established power supply and electromechanical management model can simulate faults such as generator main feeder short circuit and busbar short circuit to a certain extent, and automatically execute protection logic. The results show that the interruption conversion time of the collector voltage in the short-circuit protection is less than 50 ms, and the back-end actuator and electric pump can work normally under condition of non-emergency short-circuit faults. The designed protection logic is basically appropriate. The maximum impulse current of system short circuit is 2 898.5 A, and the maximum reverse current of motor is 38.4 A, providing reference for subsequent device selection and physical testing.

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Electronic Information and Communication Navigation
• An ADS-B Signal De-Interleaving Algorithm Based on VMD-SSA-ICA
  ZHANG Zhaoyue;DONG Guanting;BAO Shuida

  Abstract:In view of the problems that successful efficiency of de-interleaving is low under conditions of signal-to-noise ratio being low and relative delay being low for Automatic Dependent Surveillance-Broad cast signals,this paper proposes a method of ADS-B signal de-interweaving based on VMD-SSA-ICA.Firstly,the interleaved signals are decomposed by the Variable Mode Decomposition (VMD).Secondly,the sin gular spectrum analysis (SSA) is adopted to reconstruct the modes,eliminate mode aliasing,effectively analyzing the potential structure of the ADS-B signal.Thirdly,the Independent Component Analysis algorithm is used to perform de-interleaving.Lastly,the Dn-CNN neural network is used to denoise the output signal,achieving the integration of signal separation and denoising.The experimental results show that this method achieves signal decoding in a range of success rates from 60.92% to 99.94% respectively under condition of 8 to 15 dB signal-to-noise ratios.The experiments on the relative delay of different signals show that the algorithm maintains stable de-interleaving performance even with a relative delay of 0 to 10 ms.The method significantly improves the robustness and anti-interference ability of ADS-B signal de-interleaving algorithm.

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• Time Slot Allocation for Improved Learning Automata in Clustered Aeronautical Ad-hoc Network
  LI Dongxia;GAO Yi;LIU Haitao

  Abstract:In existing resource allocation schemes for aeronautical Ad hoc networks,there remain some problems that high control overhead is high and time slot is low in utilization in trans-oceanic scenario applications,an improved learning automata is proposed for slot allocation (ILASA) scheme based on the clustered aeronautical Ad hoc network.Firstly,a model of clustered aeronautical Ad hoc network is given. Secondly,a time slot frame structure is designed,the time slot allocation mode of the learning automata algorithm is improved,and the probability updating method in the reward and punishment mechanism is optimized,solving the probability selection bias problem of the learning automata algorithm by increasing the time slot reservation mechanism.Lastly,the network model is constructed on the basis of the OMNeT++ platform for simulation.The results show that the proposed scheme can reduce the resource overhead caused by the control information,effectively reduce the average end-to-end delay of the network,and im prove the network throughput and packet delivery rate.

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• A Method of Modelling on Environments Jammed with Ground Clutter Based on Terrain Matching
  SHENG Chuan; GAO Xuchen; WANG Jun; ZHAI Haolong

  Abstract:Aimed at the problems to improve the confidence in ground clutter simulation for ground-based tracking radar, a simulation method in combination of statistical models with terrain matching is proposed. First, the distribution patterns jammed by ground clutter at amplitude and power spectra in combination of actual deployment position of radar, and in full consideration of the impact of terrain obstruction on radar detection are analyzed, and a database of terrain profiles in various quantification directions, obstruction angles, and geographical features, is established. And then, ground clutter echo signal sequence generated is a match for the detection signal waveform through digital convolution. The simulation results show that this method requires low signal synthesis power, effectively controlling the scale of signal processing, and is still more conformed with the actual working conditions for radar under terrain obstruction.

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• A Method of Residual Spatially Variant Phase Error in Compensation for Airborne Curved Trajectory SAR Based on RAA
  QIU Feng1;SHEN Ruina1;DU Wangwang2;TANG Shiyang2

  Abstract:The highly-squinted airborne synthetic aperture radar (SAR) with curved trajectory has significant potential in applications characterized by disaster monitoring and resource exploration due to its flexible flight and wide-area coverage.However,the significant spatially variant phase errors are introduced by large squint angle,leading to severe defocusing or even imaging failure at the scene edges when the traditional imaging algorithms are employed.For this reason,a method of residual spatially variant phase error in compensation for airborne curved trajectory SAR based on the radius/angle algorithm (RAA) is proposed.First,an accurate model of residual spatially variant phase error is established,and its spatial distribution characteristics are deeply analyzed,and then,the analytical expression of the residual phase error in the spatial frequency domain is derived,and a simple and efficient spatially variant phase error compensation filter is designed by establishing a mapping relationship between the slow-time domain and the spatial frequency domain.The filter operates in the spatial frequency domain by using a block-wise processing strategy,effectively eliminating the spatially variant phase errors caused by the curved trajectory and large squint angle.Compared with the traditional algorithms,this proposed method significantly improves imaging quality at the scene edges while maintaining low computational complexity.The simulation and experimental results show that the phenomenon of azimuth defocusing at edge point targets is significantly suppressed,and the image resolution,the peak sidelobe ratio,and the integrated sidelobe ratio are still more close to the theoretical values,enabling the wide-swath imaging under large squint conditions.

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Aerospace Defense
• Air Target Threat Assessment Model Based on Regret Theory and Joint Multi-Criteria
  CAO Bo1;XING Qinghua2;WU Zhaolong3;LI Longyue2

  Abstract:In view of the problem that deficiencies exist in current air target threat assessment methods, an air target threat assessment model based on regret theory and multi-criteria is proposed. Firstly, an assessment index system is constructed by individual threat, individual value and threat urgency, and the entropy weight method is improved by introducing Pearson's correlation index to get the objective weights of the indexes. And then, a threat assessment model based on the three-way decision methods improved by the regret theory is established. Finally, a multi-player game model is constructed based on the different assessment criteria, and the Nash equilibrium is solved to get the target's threat categorization and ordering results. The simulation results show that the proposed method enables commanders to obtain the customized classification and the threat ranking results according to their decision-making preference, and the method is valid.

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• A Method of Optimizing Parameters of Direct Fed Coil Launcher Based on Differential Evolution Algorithm
  SHI Jianming;ZHANG Weixing;LIU Junjie

  Abstract:In order to optimize the structural parameters of the single-stage direct fed coil launcher and the energy distribution of the three-stage direct fed coil launcher, a field path coupling mathematical model and finite element analysis model of the three-stage direct fed coil launcher are established. The accuracy of the two models is calculated by adopting numerical calculation methods and finite element analysis methods. The results show that the motion data and circuit data calculated by the two models are basically consistent, proving that the two models have high accuracy. Based on the established parameterized model of the 3-level direct fed coil launcher, the differential evolution algorithm is used to optimize the structural parameters of the single-stage direct fed coil launcher and the energy allocation of the 3-level direct fed coil launcher. The results show that the iterative process is good at convergence with convergence being 60 iterations for single-stage optimization and 100 iterations for 3-level optimization. The optimization being over, the single-stage outlet speed increases from 27.5 m/s to 30.8 m/s with an output efficiency being improvement of 6%. The peak single-stage current decreases from 19 kA to 8 kA with a decrease of 57.9%. The 3-stage outlet speed increases from 56.4 m/s to 60.4 m/s with an output efficiency being improvement of 5%. The peak 3-stage current decreases from 25 kA to 10 kA with a decrease of 60%. The result show that the differential evolution algorithm has a good ability to optimize for multi-dimensional optimization problems of direct fed coil launchers.

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• Research on Online Decision-Making Method for Carrier Aircraft Maneuvering Strategy and Attack Timing Based on Neural Networks
  LI Zhilin1;2;ZHOU Hao1;2;CHEN Wanchun1;2

  Abstract:Directed against complex attack and defense confrontation between carrier aircraft and surface to-air missiles in modern warfare, this paper proposes an online decision-making method based on neural networks. In synthetic consideration of dual constraints of carrier aircraft maneuverability and target engagement, the aircraft's maneuvering strategy and the launch timing are optimized, improving the success rate of combat missions and meeting the needs of real-time decision-making. Firstly, dynamics models with anti-radiation missile, surface-to-air missile, and carrier aircraft are established, and a model of having attack and defense confrontation scenario, including all these three, is constructed. Through the simulation, the impact of different maneuvering strategies and launch timing on combat outcomes is analyzed, and the operation time is defined to measure success or failure of mission. And then, a genetic algorithm is employed to optimize the discrete-continuous hybrid parameter problem offline, obtaining the optimal carrier aircraft maneuvering strategy and anti-radiation missile launch timing, and taking such as these to construct a neural network training dataset and building a neural network model for training and validation. Finally, the effectiveness of neural network-based online decision-making is verified through simulation examples. The results show that this method can significantly expand the dominance zone of anti-radiation missiles, increase the mission success rate, and provide rapid predictions, meeting the needs of real-time decision-making.

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Military Intelligence
• A Lightweight Intrusion Detection Method of Drone Network with Interpretability
  WANG Peng;GUO Xiangke;SONG Yafei;WANG Xiaodan

  Abstract:Aimed at the problems that computational power is limit, storage space is small, and real-time is high for requirements of drones, a method of detecting interpretable drone network intrusion based on Kolmogorov-Arnold Networks (KAN), called KIDS, is proposed. Under the inspiration of Kolmogorov Arnold Representation Theorem, KAN is to utilize spline-parameterized univariate functions for replacing the traditional linear weights to dynamically learn activation patterns, enabling effective handling of feature extraction, and achieving excellent drone network intrusion detection performance with a more lightweight network structure. Furthermore, the visualization of parameterized spline functions provides insights into the model’s decision-making process during traffic feature extraction, enhancing trust in the model’s application. The extensive experiments being over on the real-world drone network traffic dataset drone-IDS 2020, the results demonstrate that the KIDS achieves superior detection performance by still lower model complexity, and exhibits obvious generalization capability in intrusion detection for surpassing type of drones.

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• A Method of Redetecting Objects Based on Dual Modulation in Long-Term Tracking
  LIU Chentao1;2;HOU Zhiqiang1;2;MA Sugang1;2;YUE Hao1;2;WANG Yunchen1;2;YU Wangsheng3

  Abstract:Being lost, object redetection is a crucial step for retracking object for long time visually, but due to background interference and the introduction of numerous similar objects, and the performance of re-detection is poor, for this reason, a method of redetecting object is proposed based on dual modulation. First, a modulator with search frame feature is designed to enhance the correlation between search frame features and the target, thereby improving the capability of the re-detection method to handle complex background interference. Secondly, a method with proposal features is designed to enhance the response of proposal features to the target, thereby improving the capability of the re-detection method to handle interference from similar objects. In order to verify the effectiveness of the method proposed in this paper, TransT and ToMP are selected as the base trackers in combination with the algorithms proposed in this pa per form two long-term visual tracking algorithms, and the experiments are carried out on four datasets, i.e. UAV20L, LaSOT, VOT2018LT, and VOT2020LT. The experimental results show that the proposed method significantly improves the long-term tracking performance of the mentioned-above two basic trackers.

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