As the core weapon of modern air combat, the air-to-air missile (AAM) has its technological evolution closely coupled with combat employment, profoundly reshaping the modern air combat paradigm. This paper reviews the development history of air-to-air missiles since the 1950s. Taking the combat employment of foreign representative AAM variants as case studies, and following a four-generation classification system of "first-generation tail-chase attack, second-generation range extension, third-generation all-aspect dogfighting, and fourth-generation intelligent interception," it analyzes the technical characteristics and combat effectiveness of each generation in terms of guidance technology, propulsion system, and anti-jamming capability. Through the analysis of practical employment cases, this paper reveals the pattern that "requirements drive technology and practice validates effectiveness," confirming the development logic of "actual requirements driving technological iteration." It provides empirical evidence for the research and development of next-generation AAM technologies such as intelligent decision-making and all-domain coordination, and offers reference for the optimization of modern air combat tactics.

This paper conducts an in-depth comparative analysis of the 2025 and 2020 editions of U.S. Army Field Manual FM 3-01, Air and Missile Defense Operations , revealing significant evolution in operational philosophy, cognitive frameworks, operational concepts, and operational employment. Based on this analysis, it assesses the development trends of U.S. Army air and missile defense in terms of missions and tasks, architecture development, engineering methodologies, and force structuring. The study finds that the 2025 edition significantly strengthens core elements such as multi-domain operations, distributed and networked operations, and full-spectrum operations, marking an accelerated transformation of U.S. Army air and missile defense toward an integrated, agile, and resilient paradigm. Its distributed, networked, and adaptive operational model, along with the coordinated development path of doctrine and technology, will profoundly influence the global air and missile defense landscape and provides important reference for other countries in constructing next-generation air and missile defense systems.

The Indirect Fire Protection Capability-High-Power Microwave (IFPC-HPM) system is a solid-state high-power microwave counter-unmanned aircraft system (counter-UAS) weapon procured by the U.S. Army from Epirus, designed for low-altitude air defense missions. The system has evolved to its second-generation variant and has undergone two experimental deployments in the Middle East and the Indo-Pacific region. Leveraging artificial intelligence-enabled control, the IFPC-HPM achieves digitally controlled waveform output and precisely defined kill zones, with the capability to defeat unmanned aerial vehicles hardened with electromagnetic shielding. This paper presents an overview of the IFPC-HPM system, encompassing its development background, evolution, operational principle, performance parameters, operational employment modes, and future development, offering reference for research in counter-UAS and high-power microwave domains.

Digital parallel battlefield, as a key tool for the innovation and upgrading of equipment development and test training paradigms under the trend of digital and intelligent transformation, is characterized by one significant feature named “sustainable evolution”. At the juncture of the rapid development of artificial intelligence technology, with the objective of continuously enhancing the application effectiveness of digital parallel battlefield through intelligent-powered SoS (System of Systems) simulation toolchains, this paper discusses the key areas of research for the integrated development of digital parallel battlefield and intelligent SoS simulation technology under new circumstances; proposes an overall architecture and technology roadmap for the study of intelligent SoS simulation; designs the functional composition of an intelligent-empowered SoS simulation system; analyzes the enabling paths and practical values of intelligent SoS simulation technology in application scenarios such as parallel simulation analysis, adversarial practicing and training, et al; and presents the main challenges and corresponding key technologies based on current domestic and international research status.

Model credibility assessment is a major research issue in the simulation field. Current credibility assessment of complex multi-domain simulation models faces numerous challenges, including inadequate awareness of verification and validation (V&V) methods, insufficient field data, and heavy reliance on multi-domain expertise. To address these challenges, this paper proposes a multi-level and multi-dimensional intelligent credibility assessment method. The method first constructs a credibility quantification model for the simulation model under assessment. Then, a decision tree for model V&V methods is designed to intelligently recommend the most appropriate V&V method for each performance parameter, thereby reducing the risk of method misuse. Subsequently, a personalized normalization method is developed to normalize the analysis results produced by different V&V methods. Finally, considering the presence of noise, errors, and faults in real-world measurement data, a comprehensive model credibility aggregation method incorporating field data credibility is proposed. The application of the proposed method is demonstrated through an unmanned surface vessel (USV) model case study. The proposed method effectively addresses the credibility assessment challenges of cross-domain complex simulation models, thereby ensuring their credibility.

To systematically evaluate the combat effectiveness of aerial platforms, this paper proposes a multi-attribute decision-making method based on combination weighting and the Pythagorean fuzzy set. To address the problem that indicator weight determination in traditional evaluation is prone to subjective bias, a combination weighting method integrating the G1 method and the improved criteria importance through intercriteria correlation (CRITIC) method is proposed to objectively modify qualitative weights. A fuzzy decision matrix is constructed using the Pythagorean fuzzy set, and the closeness coefficient of each alternative is calculated to achieve the ranking and comparison of aerial platform system performance. Experimental results demonstrate that the proposed method exhibits favorable performance in both effectiveness and practicality, providing reliable methodological support for related evaluation research.

To address the challenge of online optimal trajectory generation for interceptor missiles against near-space hypersonic targets under complex constraints, this paper proposes a trajectory shaping midcourse guidance method based on improved model predictive static programming (MPSP). Firstly, the midcourse-terminal handover conditions satisfying impact angle constraints are analyzed, and a midcourse trajectory optimization problem under complex constraints is formulated. Secondly, considering the influence of initial trajectory angles on optimization results, an adjustable initial state matrix is introduced to achieve online generation of optimal initial values. Thirdly, based on satisfying handover constraints, the performance index function incorporating state optimality is improved to realize trajectory optimization with optimal terminal velocity. Finally, numerical simulations are conducted through constructing typical hypersonic target interception scenarios, and the results verify the effectiveness and superiority of the proposed midcourse guidance strategy.

When performing visual navigation tasks in complex unknown environments, unmanned aerial vehicles (UAVs) face various security threats such as sensor noise, GPS spoofing, and dynamic obstacles, and existing methods struggle to simultaneously ensure robustness and real-time performance. This paper proposes SecNav, a multi-level visual navigation framework for safety enhancement. The framework integrates adversarial input detection, multimodal feature extraction, and safe reinforcement learning decision-making to achieve end-to-end secure navigation. Firstly, an anomaly detection module based on a variational autoencoder (VAE) is constructed, which utilizes reconstruction error and Kullback-Leibler (KL) divergence to identify spoofing attacks and anomalous data. Secondly, a multimodal feature extraction network integrating an attention mechanism and inertial measurement unit (IMU) information is designed to enhance perceptual robustness under visual degradation conditions. Finally, a constrained Markov decision process (CMDP) model with safety constraints is introduced, and the Lagrangian method is employed to optimize the balance between reward and safety constraints, achieving safety-aware decision and control. Experiments on the AirSim simulation platform and a self-built adversarial dataset demonstrate that SecNav reduces trajectory deviation by 72.5% under GPS spoofing attacks, improves the success rate of dynamic obstacle avoidance by 29.8%, achieves an attack detection accuracy exceeding 92%, and simultaneously satisfies the real-time processing requirement of 30 frames per second, validating the significant advantages of the proposed method in terms of safety and real-time performance.

To address the challenges of unmanned aircraft system ( UAV ) identification in complex electromagnetic environments, this paper proposes a feature-enhanced multi-scale attention YOLO (FEMA-YOLO) algorithm for UAV radio frequency (RF) signal detection. Firstly, the acquired RF signals of UAVs are preprocessed, and image encoding is performed through short-time Fourier transform (STFT) to form time-frequency representations. Secondly, a bidirectional feature pyramid network (BiFPN) and a multi-scale attention network (EMA) are constructed to enhance the feature extraction capability for RF signal details and improve the anti-interference capability against background noise. Finally, the time-frequency features are fed into the improved FEMA-YOLO model for unmanned aerial vehicle (UAV) type identification. Experimental results demonstrate that the proposed algorithm can effectively identify six types of UAVs, achieving a recognition accuracy of 92.1% and a mean average precision (mAP) of 93.8%.

To address the "echo eclipse" phenomenon in pulse-Doppler radar seekers and mitigate its impact on target detection and tracking performance, this paper investigates the design technique of combined pulse repetition frequency (PRF) for anti-eclipse based on engineering application requirements. A PRF pulse combination design method independent of seeker range measurement information is proposed, and a combined PRF eclipse margin model is established. Based on Cramér-Rao bound theory, the proposed PRF pulse combination method can reduce the lower bound of root-mean-square angle measurement error of pulse-Doppler seekers by 20.24% under typical simulation conditions, thereby improving the angle error detection accuracy and stable tracking performance of seekers. The proposed method exhibits high engineering value.

With the evolution of networked distributed technologies, traditional display and control software faces challenges such as rigid architecture and insufficient intelligence, making it difficult to meet the demands of multi-source heterogeneous data fusion and dynamic resource scheduling. This paper proposes a cloud-edge collaborative layered architecture for display and control software. Through front-end/back-end decoupling and intelligent reconfiguration, the scalability and reliability of the software system are improved. The back-end modules are service-oriented through a Kubernetes-based cloud-native architecture, and the front-end modules at the edge are dynamically deployed via KubeEdge. This effectively addresses the problems of inadequate real-time performance and low resource coordination efficiency in traditional display and control software.

To address the core issues of traditional incoming target intention recognition methods, including reliance on hand-crafted rules, weak anti-interference capability, and poor multi-scenario adaptability, this paper proposes an incoming target intention recognition scheme based on intelligent algorithms. Firstly, a multi-dimensional input framework covering target attributes, kinematic features, and electromagnetic features is constructed. Secondly, the scenario adaptability of mainstream intelligent algorithms is comparatively analyzed, with emphasis on designing a lightweight improved Transformer model that employs a multimodal attention mechanism to enhance key feature weighting and integrates depthwise separable convolution to reduce computational complexity. Finally, experimental validation is conducted based on an air defense system simulation dataset. Results demonstrate that the improved model achieves an intention recognition accuracy of 91.8% in complex electromagnetic interference environments, representing an improvement of 17.5% over conventional support vector machines (SVM), with recognition latency controlled within 0.7 s. The model satisfies the real-time performance and robustness requirements of combat scenarios, providing technical support for the engineering application of intelligent defense systems.

To address the requirements for multi-target spectral imaging detection, this paper proposes an intelligent target spectral detection method based on field-of-view gating. In this method, the target scene is projected onto the surface of a digital micromirror device (DMD) through an imaging lens. By utilizing the high-speed switching characteristics of the DMD and controlling the deflection angle of each micromirror, the incident beams within the full field-of-view are directed toward the imaging detection channel. On this basis, the YOLOv5 intelligent target detection algorithm is employed to detect and locate vehicle targets and output their coordinates. Subsequently, the DMD is driven to flip the orientation of the corresponding micromirror array within the field-of-view, directing the light beam toward the spectral detection channel to accomplish spectral measurements of the selected area, thereby achieving the spectral detection of the target. In this paper, a prototype is developed and integrated, and spectral calibration is performed. Experimental results demonstrate that the prototype achieves an image resolution of 700×700 pixels, a spectral detection range of 450 nm to 480 nm, a spectral resolution of 3.1 nm, and a frame rate of 20 Hz. In real traffic scenarios, the vehicle target detection accuracy reaches 95.6%, which validates the effectiveness of the proposed field-of-view gating method for intelligent target spectral detection.

To mitigate the impact of network interruptions on service transmission, this paper addresses the issues of coupling between primary-backup path collaborative configuration and load balancing, a single evaluation metric, and low solution efficiency in existing link backup methods, and proposes a primary-backup path configuration and capacity allocation algorithm based on optimal edge slack. The algorithm takes maximizing edge slack as the primary objective and minimizing the balance degree as the secondary objective. The problem is decomposed into three stages: physical path database construction, feasible solution search, and capacity allocation optimization. Optimal solutions are achieved by reducing the search space and decoupling linear and nonlinear subproblems. Simulation results on a small-scale network demonstrate that the algorithm obtains an optimal solution with an edge slack of 7 and a balance degree of 0.062 5. Compared with traditional monolithic solution methods, the proposed algorithm significantly reduces computational complexity, effectively achieves non-overlapping primary-backup path configuration and load balancing, and ensures service continuity, while also demonstrating the critical significance of search space reduction in improving solution efficiency.

To address the issues of low fitting accuracy, difficult model design, and poor adaptability to multi-dimensional inputs in launch area calculation for surface-to-air missiles during combat missions, this paper proposes a numerical fitting method for the launch area based on symbolic regression. The method employs a genetic programming algorithm to simultaneously optimize both model structure and model parameters, ultimately yielding an explicit numerical fitting model that is readily interpretable, thereby facilitating model design and integrated deployment in embedded systems. Simulation results demonstrate that the proposed method can effectively handle multi-dimensional inputs while maintaining or even improving fitting accuracy compared with previous methods.

To address the issues of reliance on maintenance personnel experience, low accuracy, and insufficient real-time performance in fault diagnosis and maintenance within traditional product support, this paper proposes an AdaBoost-CART algorithm with weight correction based on piecewise degradation theory for intelligent product fault diagnosis. This method establishes weak fault classifiers using the classification and regression trees(CART) algorithm, and introduces a dynamic weight iteration mechanism that leverages AdaBoost and piecewise degradation theory to correct weight values, thereby strengthening feature extraction for misclassified samples and forming an AdaBoost-CART classification model with strong generalization capability. Experimental results on a product engine fault dataset demonstrate that the optimized AdaBoost-CART algorithm achieves an accuracy of 94.6%, representing an improvement of 8.6 percentage points over the pre-optimization product fault diagnosis accuracy. The proposed method realizes intelligent product fault diagnosis, provides a reasonable auxiliary decision-making scheme for product maintenance support, and improves the decision-making efficiency of product support and the operational reliability of products.