Search Results (166)

Cooperative deployment of mobile agents under geometric and line-of-sight constraints gives rise to high-dimensional constrained optimization problems whose underlying physical configuration often exhibits exploitable structure. This paper develops a symmetry-driven inverse design framework that leverages two structural features of the engagement geometry—the ${\mathbb{Z}}_2\times {\mathbb{Z}}_2$ mirror symmetries of the extended target silhouette and a closed-form forward–inverse correspondence between line-of-sight-aligned burst locations and physical agent parameters—to construct low-dimensional seeds for subsequent physical parameter optimization. The framework is developed and validated on a representative naval defense instance in which a fleet of unmanned aerial vehicles (UAVs) releases spherical obscuration payloads to interrupt the line of sight between incoming mobile threats and a cylindrical extended target. Instead of searching only over the four-dimensional UAV parameter space (heading angle, speed, drop time, fuse delay), the method first specifies a desired burst location in a two-dimensional inverse space and analytically back-calculates feasible agent parameters, which are then refined by multi-start Nelder–Mead optimization in the physical parameter space. A conservative three-dimensional cylindrical line-of-sight obscuration model is developed by constructing four extreme tangent sightlines from the missile to the cylindrical target and verifying whether the spherical smoke cloud simultaneously blocks all of them. A hierarchical multi-agent task allocation framework combines a performance matrix, assignment enumeration, and joint multi-start refinement. Numerical experiments on five progressively complex sub-problems demonstrate obscuration durations of $1.362 \mathrm{s}$ (single fixed shot), $4.580 \mathrm{s}$ (optimized shot), $7.324 \mathrm{s}$ (three-shot relay), $11.140 \mathrm{s}$ (three-UAV cooperation), and $20.652 \mathrm{s}$ (full five-UAV three-missile assignment). Additional high-dimensional benchmarks, sensitivity tests, and error analyses clarify the reproducibility and limitations of the approach. Full article

In complex detection environments, unknown detection probability and clutter rate hinder accurate tracking of cluster targets. To address this issue, this paper proposes a novel multi-sensor adaptive generalized labeled multi-Bernoulli (MS-AGLMB) filter. Specifically, we consider interactions among cluster members and adopt a virtual leader–follower model to describe cluster kinematics. Given unknown environmental parameters, we employ an adaptive cardinalized probability hypothesis density (CPHD) filter to estimate the detection probability and clutter rate in real time. Furthermore, we use Gibbs sampling to efficiently truncate GLMB association hypotheses, obtaining the posterior density and solving the multi-sensor measurement partitioning problem. A joint prediction and update strategy enables simultaneous estimation of target trajectories, detection probability, clutter rate, and cluster structure. Simulation results demonstrate that the proposed algorithm achieves greater robustness in scenarios with time-varying detection probability and clutter rate, outperforming comparison filters in cluster target tracking. Full article

Adversarial attacks have been extensively studied in recent years to investigate the vulnerability mechanisms of deep neural networks and enhance model robustness and security. However, the transferability of adversarial examples across different models remains a fundamental challenge in black-box attacks. Existing ensemble attack methods primarily aggregate gradient information from multiple surrogate models through simple averaging, failing to consider gradient conflicts and cancellations among heterogeneous models, which results in poor transferability. To address this limitation, we propose a novel ensemble adversarial attack method called Region-wise PCGrad and Margin-Guided Adaptive Weighting Ensemble Attack (RPMGEA). To tackle gradient conflicts, we adopt a region-wise PCGrad method that divides gradient maps into semantically relevant regional blocks for conflict resolution. To address weight allocation issues, we directly measure transferability contributions by evaluating decision boundary changes caused by temporary adversarial examples generated from each model’s gradients across all models, thereby adaptively allocating weights to the models. RPMGEA significantly enhances the transferability of ensemble attacks, achieving average attack success rates of up to 93.7% and 90.4% on conventional and adversarial training models, respectively, and up to 88.9% on defense models, demonstrating superior performance compared to existing state-of-the-art ensemble attack methods. Full article

This study develops a new concept of computer-assisted exercises (CAX) on constructive simulation systems and how the proposed concept affects the strategy and teaching methods. The current state of affairs in the field of defense and security, both in Europe and in the world, requires the acquisition of competencies (European Qualifications Framework—EQF: knowledge, skills, independence, and responsibility), i.e., the education and training of a significantly larger number of personnel in the field of defense and security than has been the case in the last 70 years. In addition, an important specificity of today is that students need to acquire some competencies that were almost unknown until recently. Most of these competencies are the result of the rapid development of technology, which has significantly changed human life in all areas. In order to respond to the modern requirements of conducting operations, where the transfer of information both horizontally and vertically is exponentially accelerated, current concepts of preparation and implementation of education and training, of which exercises are often the most important part, need to be replaced with new concepts, and one such concept is developed in this paper. New information introduced is mostly related to the new weapons that are being introduced (unmanned systems, hypersonic missiles, weapons based on microwaves and lasers, etc.), which all result in necessary changes to the traditional approach to conducting war, i.e., tactics, techniques, and procedures (TTP). This novel exercise concept allows for the simultaneous implementation of training for up to three or four hierarchical levels (e.g., TF Div, brigade, battalion, and company) in one exercise, while in most countries, including the NATO alliance, it is still common for such exercises to be conducted according to a concept that is over 20 years old and, as a rule, is focused on the implementation of exercises for one or two hierarchical levels. This approach allows key personnel from the headquarters of units from four hierarchical levels to be simulated in real time, which is not provided by current concepts for preparing and conducting exercises. The new concept was applied as a multi-level, computer-assisted exercise (CAX) on constructive simulation systems. In addition, significant advantages of the new concept relate to the flexibility and adaptability of the proposed concept to be applied in addition to operational units and in training institutions such as academies and higher education institutions. In addition to the above, the new concept requires a shorter planning period as well as fewer total resources needed for the preparation and implementation of the exercise. The management, organizational, and technological components of the proposed exercise concept are implemented in the CAX model. The hypotheses in this paper will be tested in an applied study, which was evaluated through an external evaluation body. The implemented CAX model was tested in Croatia on the example of using exercises at the Croatian Defense Academy. Full article

This paper investigates an optimal cooperative guidance strategy for the active defense of an early-warning aircraft (EWA) escorted by two fighters against an incoming missile. The proposed framework extends classical three-body defense models (Target–Missile–Interceptor) into a more realistic four-body engagement (Target–Missile–Interceptor 1–Interceptor 2), allowing explicit coordination among multiple defenders. By projecting the 3D engagement kinematics onto two orthogonal 2D planes—a validated simplification for typical aerial combat geometries—a tractable dynamic model is obtained. Within this model, an analytical cooperative guidance law is derived using optimal control theory and the calculus of variations, minimizing a multi-objective cost function that combines miss distance, control effort, intercept geometry, and coordination terms. Extensive Monte Carlo simulations across 23 attack directions and multiple initial ranges demonstrate that the proposed method achieves an interception success rate of 99%, with an average miss distance of below 5 m. Robustness tests further confirm stable performance under target maneuver uncertainty, sensor noise, and modeling deviations. The algorithm features closed-form control commands with low computational complexity, enabling real-time onboard implementation. Full article

The fault data of the aero-engine source domain are constrained by factors such as variable operating conditions, structural coupling, fault correlations, and information attenuation. Consequently, the obtained fault features often exhibit localities. This leads to significant discrepancies in fault feature distributions between the source and target domains, resulting in poor generalization capabilities and insufficient stability in aero-engine fault diagnosis. To address these issues, an aero-engine fault diagnosis method based on Domain-Adversarial Neural Network (DANN) and Feature Interaction (FI-DANN) is proposed. Firstly, a fault diagnosis network architecture is designed based on traditional DANN by incorporating a feature interaction module into its feature extractor. Secondly, the Kronecker product is employed to fully excavate nonlinear relationships between the features, thereby increasing the number of fault features to obtain higher-dimensional and more accurate fault features. Finally, based on information entropy theory, the number of interacted features is controlled through a weighted combination, ensuring that the retained features possess greater fault information content. This guarantees the strong generalization capability and high stability of the model. The experimental results show that the best fault diagnosis accuracies of Convolutional Neural Network (CNN), traditional DANN, and FI-DANN are 79.64%, 90.00%, and 99.03%, respectively, indicating that the proposed FI-DANN can effectively integrate multi-source fault information and enhance the accuracy, stability, and generalization capability of fault diagnosis models. Full article

A novel wideband 1-bit reconfigurable transmitarray (RTA) is proposed, which is based on a substrate-integrated cavity-backed patch (SCIBP) element. The RTA element consists of a pair of SCIBP antennas, achieving wideband operational capability through the optimization of dielectric substrate thickness. To suppress surface-wave propagation between adjacent RTA elements, a substrate-integrated waveguide (SIW) is designed to function as a metallic isolation wall. A 180° phase shift is realized by dynamically manipulating p-i-n diodes embedded within the SCIBP antenna structure. When the dielectric substrate thickness is increased from 6 mm to 10 mm, the 3 dB transmission bandwidth is expanded from 10% to 33.6%. The simulation results confirm that the proposed element realizes a 3 dB transmission bandwidth of 33.6%. A prototype RTA with 100 elements is designed, fabricated, and measured. The prototype achieves a peak gain of 16.6 dBi at 4.6 GHz, accompanied by an aperture efficiency of 17.2% and a 3 dB gain bandwidth of 18.9%. Furthermore, measured scanned beams illustrate that the proposed RTA possesses good beamscanning performance. Owing to its many advantages, such as wideband operation, lightweight design, low cost, simple structure, and easy fabrication, it is particularly suitable for application in intelligent communication systems and radar systems. Full article

Achieving the multi-beam directional manipulation of electromagnetic waves and energy management of each beam has important application value in fields such as satellite communications. Existing methods for realizing multi-beam formation and energy distribution using metasurfaces have problems such as slow optimization speeds and low design accuracies. To break through this design bottleneck, a new design paradigm is proposed that introduces a 2D image matching algorithm into the design process of metasurfaces for multi-beam energy management. By using 2D grayscale images to characterize the far-field patterns generated by the metasurface array and the expected multi-beams, a wavefront characterization and matching optimization model driven by 2D grayscale images is established, ultimately achieving the customized design of beam position, intensity, and sidelobe constraints. The fully customized beam metasurface constructed using this method generates multiple y-polarized reflected directional beams under an x-polarized wave incidence, and the beam energy distribution ratio can be customized as expected. Both the simulation and test results verify the effectiveness of the proposed method. This method provides a new design idea for metasurface devices for multi-beam energy management. The meta-device has potential applications in satellite communications, holography, biomolecular detection, and radar systems. This device can also enhance the capacity of wireless communication. Full article

Malicious traffic detection in IoT environments faces dual challenges: limited labeled data and heterogeneous, complex traffic patterns. To address these limitations, we propose a malicious traffic detection framework, GADF-SRGA, which integrates Gram-angle-difference-field (GADF) imaging with meta-learning. The framework first encodes raw IoT traffic into images via GADF, preserving the spatiotemporal characteristics of malicious traffic. It then employs meta-learning on these encoded images to enable feature-space learning under scarce data. In the inner loop, Sample-Relation Guided Attention (SRGA) leverages class-label-guided supervision graphs to learn sample similarity, improving intra-class compactness and inter-class separability in the feature space. Comprehensive evaluations on public IoT intrusion datasets Malicious_TLS and ToN_IoT demonstrate the framework’s superiority and robustness, particularly under class-imbalanced conditions, over baseline methods. Full article

Multifunctional metasurfaces, capable of flexible electromagnetic wave manipulation, have become a focus of research for their high integration and utility. In particular, those operating simultaneously in transmission and reflection modes have attracted growing interest, as they integrate multiple functions within a single aperture, save physical space, and further expand wave control capabilities across full space. In this work, an inspiring strategy of transmission-reflection-integrated bifunctional metasurface by hybridizing geometric phase and propagation phase is proposed. The transmission and reflection modes can be independently and flexibly controlled in full space: the co-polarized reflection under left-handed circular polarization (LCP) incidence is governed by rotation-induced geometric phase modulation, while the co-polarized transmission under right-handed circular polarization (RCP) incidence is modulated through scaling-induced propagation phase modulation. Moreover, arbitrary amplitude modulation of the co-polarized transmission under RCP incidence can be realized by incorporating lumped resistors. As a proof of concept, a bifunctional meta-device is constructed, which can generate vortex beam carrying arbitrary topological charge for LCP reflected wave and achieve high-quality holographic imaging for RCP transmitted wave. Both the simulated and experimental results validate the feasibility of the proposed strategy, which significantly enhances the integration density of multifunctional metasurfaces while reducing inter-functional crosstalk, expanding its potential applications in electronic engineering. Moreover, it can also serve as a fundamental machine learning platform, facilitating multimodal fusion and cross-modal learning in radar signals and visual imaging. Full article

To address the issue of jitter and oscillation of guidance command during multi-vehicle cooperative engagement with maneuvering platforms, this paper proposes a novel terminal guidance law with prescribed performance constraints for multiple cooperative vehicles, which explicitly considers both transient and steady-state performance. Firstly, based on the vehicle-target relative kinematics, with time and space as the main constraint indicators, a multi-vehicle cooperative guidance model is established in the inertial coordinate system. Secondly, combined with the sliding mode control theory, cooperative guidance laws are designed for both the line-of-sight (LOS) direction and the LOS normal direction, respectively, and the Lyapunov stability proof is given. Furthermore, to counteract the impact of target maneuvers on guidance performance, a non-homogeneous disturbance observer is designed to estimate target maneuver information that is difficult to obtain directly, which ensures that performance constraints are still satisfied under strong target maneuvering conditions. Simulation results demonstrate that the proposed guidance law enables multiple coordinated vehicles to successfully engage the target under different maneuvering modes, while satisfying the terminal time-space constraints. Compared with conventional sliding mode control methods exhibiting inherent chattering, the proposed approach employs a novel PPC-SMC hybrid structure to quantitatively constrain the transient convergence of cooperative errors. This structure enhances the multi-vehicle cooperative guidance performance by effectively eliminating chattering and oscillations in the guidance commands, thereby significantly improving the system’s transient behavior. Full article

Faced with complex, ever-changing battlefield environments and diverse attacks, enabling warship combat systems to recover rapidly and effectively after damage is key to enhancing resilience and sustained combat capability. We construct a representative naval battle scenario and propose an integrated Attack-Defense-Recovery Strategy (ADRS) grounded in warship system models for different attack types. To address high parameter sensitivity, weak initial pheromone feedback, suboptimal solution quality, and premature convergence in traditional ant colony optimization (ACO), we introduce three improvements: (i) grid-search calibration of key ACO parameters to enhance global exploration, (ii) a non-uniform initial pheromone mechanism based on the wartime importance of equipment to guide early solutions, and (iii) an ADRS-consistent state-transition rule with group-based starting points to prioritize high-value equipment during the search. Simulation results show that the improved ACO (IACO) outperforms classical ACO in convergence speed and solution optimality. Across torpedo, aircraft/missile, and UAV scenarios, ADRS-ACO improves over GRS-ACO by 7.2%, 0.3%, and 5.5%, while ADRS-IACO achieves gains of 34.9%, 17.1%, and 16.7% over GRS-ACO and 25.9%, 16.7%, and 10.6% over ADRS-ACO. Overall, ADRS-IACO consistently delivers the best solutions. In high-intensity, high-damage torpedo conditions, ADRS-IACO demonstrates superior path planning and repair scheduling, more effectively identifying critical equipment and allocating resources. Moreover, under multi-wave combat, coupling with ADRS effectively reduces cumulative damage and substantially improves overall warship-system resilience. Full article

The widespread adoption of Unmanned Aerial Vehicles (UAVs) in civilian domains, such as airport security and critical infrastructure protection, has introduced significant safety risks that necessitate effective countermeasures. High-Energy Laser Systems (HELSs) offer a promising defensive solution; however, when confronting large-scale malicious UAV swarms, the Dynamic Resource Target Assignment (DRTA) problem becomes critical. To address the challenges of complex combinatorial optimization problems, a method combining precise physical models with multi-agent reinforcement learning (MARL) is proposed. Firstly, an environment-dependent HELS damage model was developed. This model integrates atmospheric transmission effects and thermal effects to precisely quantify the required irradiation time to achieve the desired damage effect on a target. This forms the foundation of the HELS–UAV–DRTA model, which employs a two-stage dynamic assignment structure designed to maximize the target priority and defense benefit. An innovative MADDPG-IA (I: intrinsic reward, and A: attention mechanism) algorithm is proposed to meet the MARL challenges in the HELS–UAV–DRTA problem: an attention mechanism compresses variable-length target states into fixed-size encodings, while a Random Network Distillation (RND)-based intrinsic reward module delivers dense rewards that alleviate the extreme reward sparsity. Large-scale scenario simulations (100 independent runs per scenario) involving 50 UAVs and 5 HELS across diverse environments demonstrate the method’s superiority, achieving mean damage rates of 99.65% ± 0.32% vs. 72.64% ± 3.21% (rural), 79.37% ± 2.15% vs. 51.29% ± 4.87% (desert), and 91.25% ± 1.78% vs. 67.38% ± 3.95% (coastal). The method autonomously evolved effective strategies such as delaying decision-making to await the optimal timing and cross-region coordination. The ablation and comparison experiments further confirm MADDPG-IA’s superior convergence, stability, and exploration capabilities. This work bridges the gap between complex mathematical and physical mechanisms and real-time collaborative decision optimization. It provides an innovative theoretical and methodological basis for public-security applications. Full article

To enhance the perception capability of radar in complex electromagnetic environments, this paper proposes an intelligent jamming recognition and parameter estimation method based on deep learning. The core idea of the method is to reformulate the jamming perception problem as an object detection task in computer vision, and we pioneer the application of oriented object detection to this problem, enabling simultaneous jamming classification and key parameter estimation. This method takes the time–frequency spectrogram of jamming signals as input. First, it employs the oriented object detection network YOLOv8-OBB (You Only Look Once Version 8–oriented bounding box) to identify three types of classic suppression jamming and five types of Interrupted Sampling Repeater Jamming (ISRJ) and outputs the positional information of the jamming in the time–frequency spectrogram. Second, for the five ISRJ types, a post-processing algorithm based on boxes fusion is designed to further extract features for secondary recognition. Finally, by integrating the detection box information and secondary recognition results, parameters of different ISRJ are estimated. In this paper, ablation experiments from the perspective of Non-Maximum Suppression (NMS) are conducted to simulate and compare the OBB method with the traditional horizontal bounding box-based detection approaches, highlighting OBB’s detection superiority in dense jamming scenarios. Experimental results show that, compared with existing jamming detection methods, the proposed method achieves higher detection probabilities under the jamming-to-noise ratio (JNR) ranging from 0 to 20 dB, with correct identification rates exceeding 98.5% for both primary and secondary recognition stages. Moreover, benefiting from the advanced YOLOv8 network, the method exhibits an absolute error of less than 1.85% in estimating six types of jamming parameters, outperforming existing methods in estimation accuracy across different JNR conditions. Full article

The application fields of high-temperature titanium alloys are mainly concentrated in the aerospace, defense and military industries, such as the high-temperature parts of rocket and aircraft engines, missile cases, tail rudders, etc., which can greatly reduce the weight of aircraft while resisting high temperatures. However, traditional high-temperature titanium alloys containing multiple types of elements (more than six) have a complex impact on the solidification, deformation, and phase transformation processes of the alloys, which greatly increases the difficulty of casting and deformation manufacturing of aerospace and military components. Therefore, developing low-component high-temperature titanium alloys suitable for hot processing and forming is urgent. This study used data augmentation (Gaussian noise) to expedite the development of a novel quinary high-temperature titanium alloy. Utilizing data augmentation, the generalization abilities of four machine learning models (XGBoost, RF, AdaBoost, Lasso) were effectively improved, with the XGBoost model demonstrating superior prediction accuracy (with an R² value of 0.94, an RMSE of 53.31, and an MAE of 42.93 in the test set). Based on this model, a novel Ti-7.2Al-1.8Mo-2.0Nb-0.4Si (wt.%) alloy was designed and experimentally validated. The UTS of the alloy at 600 °C was 629 MPa, closely aligning with the value (649 MPa) predicted by the model, with an error of 3.2%. Compared to as-cast Ti1100 and Ti6242S alloy (both containing six elements), the novel quinary alloy has considerable high-temperature (600 °C) mechanical properties and fewer components. The microstructure analysis revealed that the designed alloy was an α+β type alloy, featuring a typical Widmanstätten structure. The fracture form of the alloy was a mixture of brittle and ductile fracture at both room and high temperatures. Full article