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A Comprehensive Review of Key Technologies for Deflectable-Nose Missiles WANG Jianlei, ZHANG Zhipu, KONG Xiaojun, ZHANG Shunjia, GONG Chunlin Air & Space Defense    2025, 8 (6): 1-15.   Abstract （389）      PDF（pc） （4742KB）（461）       Knowledge map    Save Conventional air-to-air missiles with aerodynamic control surfaces have limitations in terminal interception against high-speed, highly maneuverable targets. At the same time, the presence of exposed fins also constrains the loading efficiency on fighter jets. As a feasible and efficient control approach, nose deflection control technology has gradually demonstrated considerable engineering potential. It is expected to provide valuable reference information for the development of next-generation missile control technologies. This paper first systematically reviewed the historical development and current research status of nose deflection control. Then, it provided a comprehensive analysis of research endeavors related to aerodynamic characteristics, configuration optimization, dynamic modeling, and control system design, as well as the mechanism design and simulation of nose-deflection-based missiles. Finally, this paper summarized the significant research progress and anticipated the future trends in the development of nose deflection control technology. Related Articles | Metrics | Comments（0）

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Occluded SAR Target Recognition Based on Multi-View Mutual Learning Network REN Haohao, CUI Shan, JIANG Xinyu, LIANG Shuyi, ZHOU Yun Air & Space Defense    2025, 8 (6): 25-34.   Abstract （388）      PDF（pc） （7845KB）（175）       Knowledge map    Save For the problem of SAR target recognition in occluded scenarios, this paper proposes a novel method called the multi-view mutual learning network. The proposed method is a mutual learning framework consisting of a multi-view complementary feature learning network and a recognition network, which aims to improve the recognition model's feature extraction ability through knowledge interaction at the feature level. Specifically, to enhance the recognition network's feature extraction ability, this study developed an attribute-scattering-center-guided hierarchical feature extraction method. In view of the implementation challenges with target features in occluded scenarios, a multi-view complementary learning method was employed to comprehensively characterize target features by leveraging complementary features across different SAR images at adjacent azimuth angles. Contrastive experimental results on the MSTAR dataset show that the proposed method performs well across varying levels of occlusion. Related Articles | Metrics | Comments（0）

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Design and Configuration Concept of Air Defense Weapon Command and Control Architecture for Edge Computing TIAN Ye, AN Siwei, PI Li, SUN Chengyu, LI Longyue Air & Space Defense    2025, 8 (6): 45-52.   Abstract （364）      PDF（pc） （1211KB）（281）       Knowledge map    Save Addressing the issue that traditional command and control (C2) architecture is intricate in satisfying the requirements of modern air defense combat systems, this paper analyzes the functional prerequisites of the air defense weapon C2 system and proposes a three-tier collaborative distributed architecture comprising the "core layer, edge layer, and access layer," developed in accordance with the principles of edge computing. The hardware configuration and selection scheme for the air defense weapon C2 system were designed, and the development requirements for its operating system were proposed, providing innovative ideas and methods for the intelligent upgrading of air defense weapon systems. Related Articles | Metrics | Comments（0）

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Multi-UAV Cooperative Target Search Method Based on AMDE-DMPC Algorithm CUI Siyuan, LI Hao, FAN Xiangyu, NI Lei, HOU Jiahang Air & Space Defense    2025, 8 (6): 35-44.   Abstract （342）      PDF（pc） （2421KB）（159）       Knowledge map    Save Aiming at the cooperative search problem of multiple unmanned aerial vehicles (UAVs) for dynamic and static targets, a cooperative target search method of multiple UAVs based on an improved differential evolution algorithm (Adaptive Multi-strategy Differential Evolution, AMDE) and distributed model predictive control (DMPC) was constructed. First, the battlefield environment of the mission area was described by uncertainty and the target prior probability. Based on environmental exploration benefits, target detection benefits, and obstacle-avoidance constraints, a fitness function model for collaborative target search was established. Then, the AMDE algorithm, integrating multi-strategy adaptive selection and a double-layer adaptive parameter adjustment mechanism, was designed. Combined with distributed model predictive control, the optimal search track sequence was determined to complete the cooperative target search. The simulation results demonstrate that this method can effectively increase the likelihood of target discovery and improve the timeliness of task completion, while also exhibiting good efficiency and robustness. Related Articles | Metrics | Comments（0）

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LLM-Based Intelligent Association Between Protocol Templates and Object Models ZHANG Yusheng, XU Yonghui, ZHOU Yuqi, DU Jiang, WEI Changan Air & Space Defense    2025, 8 (6): 94-102.   Abstract （269）      PDF（pc） （3512KB）（258）       Knowledge map    Save To bridge the semantic gap resulting from the transmission of external products via heterogeneous communication protocols, in conjunction with the operation of the joint test platform utilizing the built-in standard object model (SDO) subscription-publish mechanism, this paper introduces an intelligent association method and system for protocol template-object models founded on large language models (LLMs). The process took the XML protocol template and interface description model as input, and generated the intermediate representation through structured preprocessing. The local knowledge base was constructed under the retrieval, augmentation, and generation (RAG) framework to uniformly store SDO definitions, historical association pairs, and proprietary corpora. And through the “dual-channel index,” combined with sparse keyword matching and dense semantic vector retrieval, highly recalled candidates for protocol elements and SDO attributes were generated. Subsequently, a lightweight, high-performance large-scale reasoning model was adopted to perform semantic disambiguation and consistency verification on candidate pairs, and the optimal match was output alongside the prompt-word norms and rule constraints. For “strange inputs” such as abbreviations, pinyin, and mixed Chinese-English writing, a multi-agent diversion parsing strategy was introduced, significantly enhancing the robustness against non-standard expressions. The system ultimately automatically generated a standardized XML association relationship list, supporting traceable evidence fragment backlinks and threshold filtering to avoid low-correlation strong matching. The prototype software integrated the full-process modules of file parsing, knowledge retrieval, intelligent matching, and result export. In verifying typical protocol templates and object model scenarios, it demonstrates robust alignment capabilities and engineering availability across languages and naming systems, providing an efficient and scalable semantic mapping path for rapid access to external resources by the joint test platform. Related Articles | Metrics | Comments（0）

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Study on Simulation Data-Driven Fault Diagnosis Technology for Unmanned Aerial Vehicles RONG Guang, ZHANG Yexin, TANG Chao, CHEN Jinbao, ZHOU Yiling, WANG Jianyuan Air & Space Defense    2025, 8 (6): 73-84.   Abstract （214）      PDF（pc） （2160KB）（152）       Knowledge map    Save This paper addresses the increasingly complex fault diagnosis requirements of UAV (Unmanned Aerial Vehicle) systems by proposing a data-driven multi-model fusion fault diagnosis method. A deep learning model library incorporating CNNs, LSTMs, and RNNs was constructed to perform modular fault diagnosis across four key subsystems: guidance control, electromechanical, power, and airframe structure. Experiments employed a self-built simulation dataset, and model performance was evaluated using metrics including the confusion matrix, accuracy, recall, and F1 score. The results demonstrate that all subsystem models exhibit robust diagnostic capabilities, with the power subsystem achieving the highest accuracy, followed by the avionics subsystem. This technical approach offers a systematic solution for intelligent UAV fault diagnosis and can be applied to fault prediction in other complex equipment. Related Articles | Metrics | Comments（0）

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Research on Requirements and Characteristics of Airborne Platform Active Defense Missions in Future Air Combat LYU Ruiheng, ZHU Wensheng, ZHAO Hanxi, LI Xiaolong, GAO Sen Air & Space Defense    2025, 8 (6): 16-24.   Abstract （190）      PDF（pc） （9617KB）（433）       Knowledge map    Save In recent years, ongoing innovation within the military sector across tactics, technological capabilities of military systems, and equipment technologies has resulted in future air combat environments where airborne platforms will confront increasingly significant threats. These threats will be characterized by attributes such as high stealth, high intensity, comprehensive coverage, and multiple layers. As a result, the requirement for active defense capabilities has become increasingly urgent. Various international initiatives have put forward numerous concepts and initiated preliminary technical research. However, practical application in real combat conditions remains a challenge. This article focuses on the active defense mission of airborne platforms in future air combat. Starting with the threats these platforms face, it investigated the key requirements for active defense capabilities, systems, and technologies. Combining these with the latest developments in military equipment technology, this study summarizes the typical characteristics of active defense missions for airborne platforms. Related Articles | Metrics | Comments（0）

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Research on Multi-Agent Training for Maritime Joint Air Defence Based on a Multi-Algorithm Framework with Adaptive Hierarchical Sharing YE Qichang, WAN Shizheng, LI Yueshu, CHEN Zhumei, LIU Shanglin Air & Space Defense    2025, 8 (6): 121-128.   Abstract （182）      PDF（pc） （1742KB）（125）       Knowledge map    Save Multi-agent training is widely applied in modern maritime joint air defence operations. When two opposing parties independently choose different learning algorithms, it is often necessary to ensure implementation consistency and to manage resource allocation. To achieve this, a cross-agent shared fully connected layer is typically employed as a common representational foundation. However, a static choice between “fully shared” and “fully private” structures fails to balance policy consistency with individual differentiation. At the same time, discrepancies in behaviour distribution and gradient statistics across algorithms may amplify negative transfer and training variance. To overcome the above challenges, this paper introduced adaptive layer sharing (ALS) across heterogeneous algorithms, enabling learnable gating mechanisms to dynamically weight between shared and private branches at every layer. In a small-scale, single-machine experimental setup, standardized and reproducible protocols were established to record and report game outcomes and compliance. When ALS was activated, the learned gating distributions and thresholded topologies would be extracted, creating an implementable and observable engineering baseline that provides a clear structural and metric foundation for future large-scale and multi-task evaluations. Related Articles | Metrics | Comments（0）

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A High-Confidence Jamming Digital Model Construction Technology Based on Live Equipment Data-Driven Approaches LIU Yuxu, ZHANG Yexin, YAN Tianxu, ZHU Weihua Air & Space Defense    2025, 8 (6): 114-120.   Abstract （181）      PDF（pc） （1683KB）（143）       Knowledge map    Save Addressing the issue of low consistency and credibility of traditional interference digital models compared to indoor interference generators, this paper proposes an intelligent optimization technique for interference digital models based on indoor data verification. Through the development of a multi-domain loss function that incorporates temporal, spectral, and energy aspects as the verification metric, coupled with the implementation of a Bayesian optimization strategy for the intelligent tuning of the interference digital model parameters, the congruence between the interference digital model and the indoor interference generator has been attained at a level of up to 90%. This high-consistency interference digital model, combined with indoor data, can be used to construct digital interference scenarios. Related Articles | Metrics | Comments（0）

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Research on Simulation Methods of Turbulence Effects in Infrared Scene Simulation WU Xin, HUANG Zhonghua, ZHOU Yuxuan, FAN Haojiang, LIU Delian Air & Space Defense    2025, 8 (6): 62-72.   Abstract （170）      PDF（pc） （4556KB）（93）       Knowledge map    Save In intricate and changing atmospheric conditions, turbulence may cause infrared images to appear blurry, distorted, or jittery. These effects significantly impact the performance evaluation of infrared imaging systems. A critical consideration in infrared scene simulation is an area that has garnered increasing attention from researchers in recent years. To address this challenge, this paper proposes a turbulence simulation model for dynamic infrared scenes based on the refractive-index structure constant. First, using measurement data and an infrared radiation transfer model, a three-dimensional, real-time infrared simulation scene was constructed. Then, by modeling variations in the atmospheric refractive index caused by non-uniformities in air density and temperature, the blurring and jitter effects induced by turbulence were simulated, enabling the generation of high-quality, highly realistic turbulence-degraded images. This study introduces an innovative method for simulating turbulence effects in dynamic infrared scenes, offering both theoretical insights and data to support further investigation into how turbulence influences infrared imaging systems and how to potentially mitigate its effects. Related Articles | Metrics | Comments（0）

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Inversion and Interpretation of Target Electromagnetic Scattering Characteristics Based on Measurement and Computation ZHANG Xiaokun, YAN Tianxu, PENG Guopeng, WANG Long, ZHANG Yixuan, ZHAO Xunwang Air & Space Defense    2025, 8 (6): 85-93.   Abstract （156）      PDF（pc） （2739KB）（340）       Knowledge map    Save This paper systematically investigates a measurement-based computational approach for the inversion and interpretation of target electromagnetic scattering characteristics, focusing on the issues of stringent site requirements, high costs, and the limited capacity to reveal the physical mechanisms of target scattering associated with conventional Radar Cross Section (RCS) measurement techniques. First, high-precision electromagnetic near-field data of the target were acquired within a limited distance. Subsequently, the target's far-field scattering characteristics were accurately reconstructed using near-field-to-far-field transformation techniques. Then, the transformed data were processed by a parametric scattering-center extraction algorithm to invert the macroscopic scattering response into a set of attributed scattering-center parameters with precise physical meanings. By comprehensively analyzing the attributed scattering centers as well as the Inverse Synthetic Aperture Radar (ISAR) image, the link between“data measurement” and “physical mechanism interpretation ” was established, providing practical support for potential applications such as target shape optimization, recognition algorithm development, electromagnetic characteristic modeling, and professional wargame simulation. Related Articles | Metrics | Comments（0）

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Research on Light Transmission Performance in a Low-Temperature Nitrogen Environment CAI Aifeng, TIAN Yi, TIAN Yusong, SHE Shaobo, LI Chunyu, WU Jingyi Air & Space Defense    2025, 8 (6): 103-113.   Abstract （147）      PDF（pc） （5711KB）（149）       Knowledge map    Save In a semi-physical simulation test rig for infrared guidance control systems, a low-temperature nitrogen environment is required to emulate the cold background of space. To ensure that the distortion of the light beam emitted by the infrared target simulator during transmission in nitrogen remains within the permissible range of the experiment, this paper establishes a flexible cold chamber simulation model for a low-temperature nitrogen environment. After experimental verification, the internal temperature distribution of the freezing chamber under various conditions was determined, and its optical transmission performance was subsequently evaluated based on this. Results show that under the rated operating condition, with an inlet velocity of 0.05 m/s and an inlet temperature of 153 K, the temperature standard deviation within the beam is 1.48 K, and the peak-to-valley optical path difference (Rδ) is 1.23 μm. The Strehl ratio (S) was 0.965, indicating a minimal effect of low-temperature nitrogen on optical transmission. Inlet velocity has a more significant influence on optical transmission performance. When the velocity ranges from 0.02 m/s to 0.07 m/s, the Rδ and S vary between 0.47-3.43 μm and 0.845-0.978, respectively. Notably, when the inlet velocity decreases to 0.015 m/s, the S drops to 0.798, resulting in the system degrading into a non-ideal imaging state. Additionally, an increase in position angle caused by the rotation of the upper cryogenic chamber decreases the threshold for temperature standard deviation in ideal imaging systems. Compared to a 0° position angle, this threshold decreases from 3.53 K to 3.23 K. Related Articles | Metrics | Comments（0）

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Numerical Simulation Research on the Characteristics and Influence Factors of High-Velocity Fragment Penetrating Panel CHEN Anran, JIA Dao, HE Wei Air & Space Defense    2025, 8 (6): 53-61.   Abstract （108）      PDF（pc） （3943KB）（137）       Knowledge map    Save An Adaptive algorithm of FEM-SPH was used in this study to numerically simulate the high-velocity fragment penetrating the panel with different materials, and experimental results calibrated the numerical results. The calculation error was within 15%, and the calibrated model could be used to predict phenomena or reproduce experimental results. The penetration characteristics of tungsten fragments into the Q235 steel panel were examined. The influence of the fragment's mass, aspect ratio, impact velocity, and the panel's thickness on penetration ability was analyzed. The research indicated that appropriate combinations of fragment mass and size could be selected within the threshold range of fragment kinetic energy during the design of warheads, thereby increasing the number of damage elements and the probability of perforation. The research could provide a basis for the design of relevant warhead models. Related Articles | Metrics | Comments（0）

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Research on Target Recognition Method Based on Multi-Source Information Fusion CONG Xiaoyu, YANG Jiayi, SHAN Shichen, ZUO Qian Air & Space Defense    2026, 9 (1): 12-19.   Abstract （82）      PDF（pc） （866KB）（412）       Knowledge map    Save During multi-source information fusion, efficiently combining features from High Resolution Range Profiles (HRRP) and Inverse Synthetic Aperture Radar (ISAR) images is challenging due to the difficulty in fusion, limited sample availability, and the open-set recognition problem. To address these issues, a spatial target recognition method based on feature alignment and data augmentation was proposed. Firstly, Principal Component Analysis (PCA) was adopted to reduce the dimensionality of HRRP and extract time-frequency features. The Pauli decomposition was then utilised to expand the data of polarised ISAR images. After that, an improved ResNet18 network and a Transformer fusion module were constructed to align and fuse HRRP and ISAR features. Finally, the OpenMax open-set recognition framework was introduced, and the Weibull distribution was used to model class boundaries to achieve discrimination of unknown classes. Experimental results show that the proposed method achieves 90.43% accuracy in closed-set recognition and 91.39% rejection rate for unknown classes in open-set recognition, verifying its effective recognition and generalisation abilities in complex scenarios. Related Articles | Metrics | Comments（0）

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Robustness of Radar Intelligent Recognition Models Under Adversarial Samples Attacks SHEN Tong, CHEN Jingxian, ZHONG Ping Air & Space Defense    2026, 9 (1): 46-51.   Abstract （78）      PDF（pc） （719KB）（221）       Knowledge map    Save Addressing the problem of insufficient robustness of the intelligent recognition model of radar High Resolution Range Profile (HRRP) under adversarial sample attacks, a lightweight enhancement method was proposed in this study. Firstly, a comprehensive analysis was conducted using the Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and a black-box migration attack to assess the vulnerability of the lightweight Convolutional Neural Network (CNN). Secondly, a cascaded defense strategy of “fast adversarial training + input denoising auto encoder + post-anomaly detection”was established. Finally, countermeasure experiments were carried out using three types of air targets and 6,000 sets of measured samples. The results show that this strategy can reduce the attack success rate to 9.2%, sacrificing only 2.1 percentage point of the cleaning accuracy, and increasing inference delay by less than 20%. It achieves a stable balance among model size, real-time performance, and robustness, providing a practical solution for the anti-interference design in radar-intelligent recognition systems. Related Articles | Metrics | Comments（0）

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Uncertainty Quantification Approach for Aerial Target Recognition Based on Hierarchical Bayesian Models MA Yonglin, LI Hao, XIONG Wei, LI Lingzhi, TANG Jingmian Air & Space Defense    2026, 9 (1): 20-27.   Abstract （77）      PDF（pc） （1692KB）（250）       Knowledge map    Save This paper proposes a recognition framework based on a hierarchical Bayesian model to address the challenges associated with fragmented prior knowledge and the absence of uncertainty quantification in decision-making processes for aerial target recognition within complex electromagnetic environments. By developing a three-tiered hierarchical structure encompassing "measurement noise-individual characteristics-class commonality", the intra-class physical variability of target Radar Cross Section (RCS) and sensor random noise were explicitly modelled as probability distributions, representing a novel contribution. Posterior inference was performed using Markov Chain Monte Carlo (MCMC) methods, simultaneously outputting target-class probabilities with confidence intervals. Simulation results show that under harsh observation conditions at 5dB SNR, the recognition accuracy reaches 78%, improving by 6% to 10% over Support Vector Machine (SVM) and Naive Bayes classifiers. In small-sample scenarios (5 training samples per class), the accuracy advantage increases to approximately 13%. The 95% confidence interval coverage rate exceeds 88%, validating the effectiveness of uncertainty quantification. The proposed method provides a practical pathway to robust target recognition within complex battlefield environments characterized by "small-sample + high-noise" conditions. Related Articles | Metrics | Comments（0）

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Density Cluster-Based Clutter Removal Technology for Millimeter-Wave Radar Target Point Cloud LIU Qi, HE Yifei, GU Ming, CHEN Zihao, LI Yunhao, WANG Tao Air & Space Defense    2026, 9 (1): 63-72.   Abstract （62）      PDF（pc） （4745KB）（175）       Knowledge map    Save This paper introduces an improved adaptive DBSCAN clustering algorithm to tackle the issues of point cloud clutter removal and sparse target classification in radar imaging systems, which are traditionally handled by signal processing methods. The proposed method constructed a Euclidean distance matrix for classification, rapidly identified central sampleswithin categories, detected and eliminated anomalous stray points, and adaptively adjusted the neighbourhood density and radius parameters for future frames based on the Euclidean distances and mutation indices of the central points. Initially, the engineering advantages of the improved algorithm were validated through simulation experiments, followed by further verification using real-world road scene data to confirm its practical effectiveness. Experimental results show that the proposed algorithm effectively eliminates clutter from target point clouds and dynamically adjusts clustering parameters to reduce sparse classification errors in targets. Related Articles | Metrics | Comments（0）

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A Survey of Foreign Near-Space Defense Equipment Development in 2025 LAI Wenxing, CHEN Tianyu Air & Space Defense    2026, 9 (2): 97-104.   Abstract （51）      PDF（pc） （5647KB）（437）       Knowledge map    Save With the maturation and accelerated fielding of hypersonic strike weapon systems worldwide, traditional air and missile defense architectures face the risk of capability negation. The United States, Russia, and other military powers are actively advancing top-level planning, operational concept development, and system acquisition for near-space defense. Leveraging existing missile defense assets, these nations are constructing hypersonic defense architectures, developing novel sensors and interceptors, and seeking to establish segmented, multi-layered defense systems in the near-space domain to fill critical capability gaps. This paper analyzes the current development of foreign near-space defense systems, evaluates U.S. near-space fire interception performance metrics, examines typical U.S. defensive operational processes, and contrasts the divergent development approaches of the United States and Russia in hypersonic defense, offering insights for the construction of indigenous near-space defense capabilities. Related Articles | Metrics | Comments（0）

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Deep Learning-Based Infrared Ship Target Wake Matching and Detection Algorithm CHEN Liangwen, ZHU Yuxin, SHEN Tao, YU Yifan, LING Xiao, SHENG Qinghong Air & Space Defense    2026, 9 (1): 80-90.   Abstract （50）      PDF（pc） （4260KB）（296）       Knowledge map    Save This paper proposes a deep learning-based infrared ship-and-wake detection algorithm to address missed and false detections of low-emission, small targets in complex sea-sky backgrounds. The algorithm enhanced the YOLO network by incorporating a dual attention mechanism that suppresses feature maps within the YOLOv8 architecture. In addition, a ship-wake matching module was developed, leveraging more prominent wake features to assist ship detection, effectively reducing false alarms and missed detections of weak and small ships in complex backgrounds. Finally, a ship dataset was constructed for testing and analysis. Results show that the proposed algorithm achieves 98% precision and a high detection speed, demonstrating strong robustness in detecting weak infrared ship targets and significantly enhancing detection performance. Related Articles | Metrics | Comments（0）

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Model and Stability Analysis of Position-Attitude Control for Small Attack UAVs Based on ADRC WU Xianfeng, ZHANG Hangwei, WANG Yaping, LIU Xing, WEN Qingpeng Air & Space Defense    2026, 9 (2): 18-32.   Abstract （49）      PDF（pc） （2675KB）（136）       Knowledge map    Save Based on the Newton-Euler theorem, this paper constructs a control model for small attack Unmanned Aerial Vehicles (UAV), integrating the Dryden turbulence wind field and recoil force models. Position and attitude controllers are designed using Active Disturbance Rejection Control (ADRC) theory. Comparative experiments with traditional PID control demonstrate that the ADRC controller possesses stronger anti-disturbance capability and faster dynamic response under wind disturbances and step disturbances. Stability factor analysis indicates that: with increased firing angle, the maximu•m yaw and pitch angles gradually decrease, while the maximum roll angle exhibits a nonlinear variation of initial increase followed by decrease; with increased firing frequency, the pitch angle disturbance presents a superposition effect, aggravating attitude deviation. The research results provide a new approach for high-precision stability control of small attack UAV in complex battlefield environments, and have important application value in military reconnaissance, precision strike and other fields. Related Articles | Metrics | Comments（0）