This paper comprehensively and systematically analyzed the theoretical evolution, model construction, effectiveness evaluation, and optimization methodologies of kill chains and webs. First, the fundamental distinctions between kill chains and kill webs were introduced via conceptual comparative analysis. Then, from the perspective of four key modeling challenges: structured information representation, cooperative system optimization, dynamic adaptability, and intelligent decision-making， the construction mechanisms and technological breakthroughs of various models were investigated. Quantitative evaluation methods for key dimensions, including survivability, resilience, and node importance, were summarized. Following this, strategies for dynamic reconstruction optimization and multi-objective conflict resolution were studied. Finally, future development trends of kill chains and kill webs were projected.

When conducting flight perception and decision-making tasks, such as target detection and recognition, and online route planning, intelligent aircraft encounter key scenarios that affect flight safety, including false and missed target alarms, and obstacle avoidance failures. Furthermore, due to the combination explosion of the state space of the data-driven intelligent model algorithm and the black-box characteristics of the computing logic, it is challenging to discover and identify its cognitive deception scenarios. In this study, the spoofing attack method was applied to generate targeted micro-disturbances in the system input, creating scenarios that pose risks and challenges to intelligent aircraft. The intelligent aircraft system was then constantly trained to test its operational limits, thereby evaluating safety-critical boundary scenarios for flying objects. This method revealed potential vulnerabilities that standard testing methods may not be able to detect. Meanwhile, the deceptive tests of intelligent aircraft in different risk scenarios ensured the safety and performance in the most challenging situations. The generation of these complex scenarios is crucial for enhancing the robustness of autonomous flight systems and preparing them for a broader range of real-world challenges.

To analyze the development patterns of airstrikes, this paper examined recent local conflicts, including the Gulf War, the Kosovo War, the Nagorno-Karabakh conflict, and the Russo-Ukrainian conflict. The modern battlefield's operational domain is expanded from the physical domain to encompass the information, cognitive, and social domains. In terms of air strike equipment, there is a trend towards differentiation based on stealth technology, focusing on near-space hypersonic and low-altitude, low-speed extremes. Low-altitude dominance has become a focal point of contention. Air strike methods are closely linked to battlefield space, characterized by multi-domain coordination, mixed equipment use, and multiple-wave saturation. The "complexity" led by "scale + intelligence" is a new form of combat effectiveness.

Continuous rotating detonation combustion, which is characterized by high efficiency, safety, and environmental friendliness, and is capable of sustaining combustion with a single ignition, has gradually attracted the attention worldwide and is considered a revolutionary aerospace propulsion technology. In this paper, firstly, the power technology advantages, detonation mechanism, and application directions of rotating detonation engines were systematically reviewed. Then, the key research progress in recent years on ramjet engines, rocket engines, and gas turbine engines that use continuous detonation mode was summarized. Finally, the key issues faced in the engineering application of continuous detonation combustion engines were studied, and the future development trend was projected.

Morphing technology applied to hypersonic vehicles in near-space can enhance flight performance, penetration capability, and combat effectiveness, obtaining a broad application prospect. This paper focused on the application prospects and challenges of morphing technology in hypersonic near-space vehicles. It comprehensively summarised the research progress and status of morphing technology from three aspects: morphing methods, morphing drive, and morphing control. It delved into the characteristics and application prospects of hypersonic near-space vehicles, and systematically identified three significant difficulties: overall vehicle design, morphing mechanisms design, and flight control design. To resolve the above findings, this study proposed several key future technologies, including multidisciplinary coupled overall design for morphing vehicles, integrated deformation-bearing-thermal protection structure design, high-response-speed/high-precision/high-power-to-weight-ratio servo design, high-precision aerodynamic prediction and modelling, rapid aerodynamic elasticity analysis methods, and high-precision control under strongly coupled nonlinear conditions.

The kill web is a new, highly dynamic, and flexible combat form that will be deployed in future wars. A missile launching system, which serves as a fire execution terminal, is an essential part of the kill web. Based on the characteristics of the kill web, this paper analyzed and investigated their deployment in various domains, including missile loading and launching, bidirectional information application, autonomous status monitoring, fault prediction and handling, scenario-driven enablement, self-protection capability, and system architecture. The intelligentization development prospects for missile launching systems in the kill web combat style were systematically studied.This study provides a reference for optimizing and upgrading the traditional missile launching system, as well as for efficient network access.

Addressing the bottleneck of uncrewed aerial vehicles (UAVs) in complex electromagnetic environments, this paper systematically analyzed the classification and action mechanisms of electromagnetic interference (EMI) sources, EMI coupling paths, and nonlinear responses within UAVs. A multi-scale interference theoretical framework to address UAV anti-EMI issues was proposed for constructing. Five key anti-interference technologies were investigated: algorithm-level anti-interference, electromagnetic shielding, dynamic filtering, system-level collaborative protection, and optical fiber transmission technology. Respectively, algorithm-level anti-interference focused on integrating lightweight models with edge computing; electromagnetic shielding aimed to break through the low-frequency efficiency bottleneck; dynamic filtering explored the fusion of neural networks and bionic mechanisms; system-level collaboration established a closed-loop system of “interference identification-dynamic suppression-system reconstruction”; and optical fiber technology realized physical-layer signal isolation. This research offers theoretical and technical solutions for UAV engineering applications in environments with substantial electromagnetic interference, and is of significant importance for enhancing the electromagnetic compatibility of UAVs.

Driven by the rapid development of command information systems, the future space warfare is accelerating, along with the intensity of confrontation. As the core hub of the combat system, the command information system faces threats, including physical destruction and cyberattacks. Focusing on the failure, paralysis, and even damage of some nodes or units in the command information system, the basic characteristics of the new generation of resilience combat system capabilities were studied from the perspective of capability generation. The resilience architecture of the command information system was developed, and the technical layout of the resilience combat system was preliminarily planned, providing a basic framework for building a resilient combat system.

Distributed collaborative air defense is different from centralized defense models, marking a substantial evolution in modern air defense philosophy. It aims to counter increasingly complex and diverse air threats, enhancing system survivability,mission flexibility, strike speed, and adaptability in complex environments. This paper first explored the operational concept of distributed collaborative air defense, reviewing its background and necessity. Then, the requirements for distributed defense from both weapon system and missile perspectives were investigated, and corresponding defensive operational models were proposed. Finally, the key technological characteristics of the distributed collaborative air defense model at both the system and missile levels were examined and identified, giving insights for the future development of air defense missile weapon systems.

There are limitations when using a single approach to assess the aerial target threat. This paper introduced an improved AHP-CRITIC-TOPSIS method for assessing aerial target threats. Initially, a linear weighting approach based on the Analytic Hierarchy Process (AHP) was used to determine the static threats. Subsequently, a combination method was utilised to perform a dual analysis of subjective and objective weights for dynamic threats, which combined the clustered AHP and the objective process of Criteria Importance　Through Inter-criteria Correlation (CRITIC).After that, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied to calculate the dynamic threats. Besides, the aerial target threats were categorised into five levels, which were analysed from the perspective of urgency and the corresponding air defence combat requirements. Finally, acase study was conducted to validate the feasibility of the above assessment model. The results showed that the model could integrate both subjective and objective threat characteristics, giving more comprehensive assessment results across multiple dimensions.

Unmanned Aerial Vehicles (UAVs) possess inherent advantages including high maneuverability, compact size, ease of modification, low-altitude slow flight capability, and adaptability to complex environments. These features enable tasks to be completed efficiently and gather information effectively. However, the same advantages also introduce security risks from unauthorized UAVs operating in restricted areas, which are compounded by the significant interception challenges posed by their high speed, low altitude, and highly maneuverable characteristics. To address these challenges, this study focused on the critical technical problems of autonomous decision-making and trajectory control for high-speed, precise UAV interception. A method for intercepting target UAVs using an interceptor UAV was proposed, whose core approach was an end-to-end deep reinforcement learning (DRL) network framework. This framework utilized the proximal policy optimization (PPO) algorithm to train a neural network controller that directly maps perceptual information to forces and torques controlling the quadrotor UAV. To optimize interception performance, a novel reward function based on reward shaping techniques was designed. This function enabled the interceptor UAV to achieve faster, smoother, and more precise interception trajectories. Experimental results demonstrate that the proposed method allows high-speed interception and achieves superior interception accuracy. Throughout interactions within the environment, the interceptor UAV demonstrates robust adaptive adjustment and real-time decision-making capabilities. This study verifies the effectiveness of the end-to-end DRL-based UAV interception solution. The method efficiently and precisely accomplishes high-speed interception tasks, while its end-to-end nature significantly reduces reliance on precise UAV dynamic models.

By integrating intelligent algorithm-driven image processing techniques, computational imaging has the potential to transcend the limits of conventional hardware-centric optical systems, enabling optical systems to achieve high performance and a compact design. Focusing on the image reconstruction requirements in lightweight infrared single-lens computational imaging, this study investigated lightweight model deployment methodologies tailored for edge AI chips. Through targeted operator optimisation, model pruning, and quantisation implemented on edge devices, the deployed U-Net reconstruction model achieved a 52.3% reduction in parameters and a 60.3% reduction in computational operations, resulting in a 56% acceleration in edge processing frame rate while sacrificing only 0.91 dB in PSNR and 0.021 in SSIM. Further architectural simplification allowed ultra-high-speed video-rate on-chip image reconstruction exceeding 95 FPS, at the cost of just 1.3 dB PSNR and 0.018 SSIM. The experiments examined edge hardware acceleration for computational single-chip infrared camera reconstruction algorithms. This study provides technical references for engineering applications of lightweight infrared computational imaging systems.

To resolve cooperative encirclement by multiple missiles against a manoeuvring target in three-dimensional space, this study proposed an impact-time-control cooperative guidance using proximal policy optimisation (PPO). Firstly, the impact-time-control cooperative guidance model was constructed based on the extended proportional guidance, and the cooperative guidance time error term was improved. Then, the state and action space models for the Markov Decision Process were designed, and the reward function was constructed as a variable-step model combining dense and sparse rewards. The cooperative guidance model was trained using PPO, mapping the guidance state information to the cooperative guidance law. Finally, a multiple-missile cooperative encirclement scenario was established, showcasing the cooperative guidance's ability to achieve model-free, end-to-end coordinated attack timing. Monte Carlo experiments further verified the robustness of its guidance in disturbed environments.

To resolve the trajectory design problem of morphing glide vehicles during Mars atmospheric entry, this study investigated a trajectory optimization method that allows the morphing degree to be used as an auxiliary control variable. An aerodynamic model for the morphing vehicle was established, and the Mars entry optimal control problem was transformed into a nonlinear programming problem using the Gauss pseudo-spectral method. Under multiple constraints, trajectory optimization simulations were conducted with the flight range taken as the performance index. Simulation results show that incorporating the morphing degree as a control variable enables effective trajectory planning for Mars entry. Compared to fixed-configuration vehicles, the morphing vehicle reduces drag through adaptive shape transformation, thereby decreasing mechanical energy loss during the initial flight phase. The flight range was improved by 10.4%, achieving superior performance metrics.

Aiming at the collaborative optimisation of radar anti-jamming and clutter suppression in complex electromagnetic environments, this paper proposeed a triple-parameter agile linear frequency modulation (LFM) waveform design and joint signal processing method. By dynamically varying the initial frequency, pulse width, and pulse repetition interval (PRI), the signal's periodicity was disrupted, enhancing its low probability of intercept and anti-jamming capabilities. To address the clutter coupling effects and non-uniform sampling issues introduced by parameter agility, a sparse reconstruction model based on compressed sensing orthogonal matching pursuit (CS-OMP) was established, enabling the recovery of Doppler frequencies for non-uniform slow-time dimension signals, thus facilitating velocity and distance measurements. In addition, least mean square (LMS) adaptive filtering was integrated to suppress clutter interference. This method significantly enhances the target detection probability and parameter estimation accuracy in cluttered environments while retaining anti-jamming advantages, providing theoretical support for optimising radar in complex electromagnetic warfare scenarios.