For the three-dimensional scene visualization of simulating and reproducing the explosive shock experiments, a virtual simulation system,which is designed for studying and predicting the damage process and effects of explosive shockwaves on targets, is built based on virtual simulation technology and the related explosive field calculation models. The system adopts a layered architecture, covering the scene management, interaction design, power calculation and visualization simulation, and supporting the flexible configuration of explosion parameters, dynamic loading of target models and multi-perspective interaction analysis, which can adapt to different types and scales of explosion scenes. Furthermore, based on Niagara particle system and physics engine real-time rendering technology, the system accurately characterizes the flame, smoke, shockwave propagation and target damage effects, such as concrete crushing, vehicle structure deformation, etc., and optimizes the observation clarity through the dynamic smoke concentration adjustment function. The simulation system provides a highly realistic graphical interface to observe the dynamic processes and physical phenomena of explosive shocks, and supports the multi-perspective and scalable observation in 3D scene. This enhances the intuitiveness and depth of understanding in research, significantly reducing the time and cost of actual experiments, and offering a safe, reliable and cost-effective research method.

Microbially induced calcite precipitation (MICP) is effective to strengthen the calcareous sand foundation of reef fortifications via in-situ construction,dynamic response,and the dynamic characteristics and numerical model of MICP-treated calcareous sand are essential to the reef defense design and damage evaluation.The MICP cementation experiment is conducted on calcareous sand from the South China Sea.The strain rate effect of MICP-treated calcareous sand is evaluated through quasi-static uniaxial compression and dynamic mechanical testing of a split Hopkinson pressure bar (SHPB).A user subroutine VUMAT is developed based on the microplane model M7 for calibrating the material parameters.Compared with the calcareous sand penetration experiments,the numerical simulations demonstrate an increase in the penetration resistance of calcareous sand.The results reveal that the unconfined uniaxial compressive strength of MICP-treated calcareous sand specimen is 12.31MPa,and the dynamic increase factors are 1.117,1.485,and 1.828,respectively,at strain rates of 426s^-1,1150s^-1,and 1712s^-1 on the Hopkinson bar.Strain rate effect has a negative influence on the penetration depth of M7 constructive model by 4.2% DOP contribution.Compared with ordinary calcareous sand,the depth of penetration of MICP-treated calcareous sand is reduced by 40.11% on average,and the average target static resistance increases from 5.21MPa to 11.89MPa,indicating a significant increase in the resistance to penetration.These findings provide the experimental data and simulation model reference for damage analyses of reef fortifications subjected to high-impact loadings.

The complex nature of missions executed by the Armed Police Force imposes higher requirements on the precision and control of strike effects of firearms.This paper focuses on the design and key technologies of intelligent non-lethal electromagnetic constant kinetic energy strike weapon systems.A “human-weapon integration” precision strike model is constructed by integratiing artificial intelligence，electromagnetic launch，and constant kinetic energy strike theories to achieve the dynamic control of non-lethal kinetic energy output.A hierarchical framework and operational mechanism based on the “perception-decision-execution” architecture are proposed by reviewing the global development status of intelligent firearms and analyzing the design necessity and intelligent connotation of weapon systems.Key technologies including line-of-sight stabilization，trajectory computation with terminal energy threshold，and electromagnetic launch control are thoroughly analyzed.The future development directions and potential contradictions of weapon systems are presented，transitioning from theoretical research to practical combat applications and providing effective support for equipment upgrading and operational effectiveness enhancement of Armed Police Force.

To study the energy release characteristics of energetic materials after excitation and ensure the safety of the test, a double-layer asymmetric cylindrical explosion protection device is designed with capability of containing 1kg TNT equivalent detonation. Through the structural design, simulation analysis using LS-DYNA finite element software, and full-equivalent detonation dynamic response test with 1kg TNT, the safety of the explosion protection device is evaluated by comparing the theoretical predictions, experimental data and simulated results. Simulated and experimental results demonstrate that the structural design of the double-layer asymmetric cylindrical explosion protection device is reasonable with a sufficient safety margin. In the full-scale 1kg TNT detonation test, the stress corresponding to the measured outer wall strain at typical positions is lower than the yield strength of shell material of the protection device. The peak reflective overpressure measured on the inner l at typical positions is within the range calculated by the empirical formulas. The explosion resistance strength of the protection device meets the test requirements. The design, analysis, and experimental methods in this paper can provide useful references for the design and verification of similar explosion protection devices.

Energetic high-entropy alloys (EHEAs) hold significant promise as active fragmentation materials for enhancing the penetration, ignition, and damage efficacy of weaponry and ammunition systems. Nevertheless, a pressing need exists for in-depth investigations into their damage mechanisms and corresponding effects. To investigate the damage effects of EHEAs and the characteristics of the fragment cloud during the deflagration process, a two-stage light gas gun is employed to conduct damage experiments on spaced aluminum targets, utilizing the TiZrHfTa_0.5 high-entropy alloy at varying impact velocities. High-speed photography is utilized to document the perforation deflagration behavior, and theoretical analyses are performed to elucidate the formation process of the debris cloud behind the target. Additionally, smoothed particle hydrodynamics (SPH) numerical simulations are carried out to supplement the experimental findings. The experimental results demonstrate that EHEAs exhibit intense exothermic detonation behavior upon exceeding a critical impact velocity. As the penetration velocity increases, a positive correlation is observed between the impact velocity and the diameter of the entry hole on the target, leading to an augmented penetrative and damage area on the rear target. This phenomenon can be attributed to the exponential growth in the number of fragments within the debris cloud with increasing velocity, and the velocity gradient significantly promotes the release of energetic characteristics, thereby magnifying the overall damage effects. Based on a comprehensive theoretical analysis and experimental data, a modified empirical formula for predicting the perforation diameter is proposed. Fragment size analysis further reveals that the refinement of fragments facilitates the synergistic interaction between chemical and kinetic energy, ultimately enhancing the damage performance of EHEAs.

The ceramic/fiber composite ballistic plates are widely used in personal protection equipment. Studying the performance of ballistic plate subjected to multi-impacts is of great significance for reducing the number of casualties in the battlefield.This paper investigates the Al₂O₃/UHMWPE composite ballistic plate,focusing on numerical simulations conducted for various bullet impact points.The reliability of the simulated results is validated by comparing with the experimental results.The relationship between the energy absorption and damage characteristics of the ballistic plate is derived from the ceramic and fiber damage results,and the penetration probability of the next impact is effectively calculated.The results indicate that the damage patterns of ballistic plates are roughly the same and the impact resistance of ceramic layer at the joint decreases.when the impact point of the first bullet is located at the center of a ceramic plate,within the gap between two plates,or at the gap of quadrilateral jointing.Specifically,when the impact point of the first bullet is located at the center of ceramic plate,the subsequent bullets tend to penetrate through the gap between adjacent plates.As the proximity between successive impacts decreases,the energy absorption by the ceramic layer diminishes while the energy absorbed by the fiber layer increases.When two impact points are located parallelly and diagonally apart,the middle ceramic plate does not suffer macroscopic damage due to the joint structure.When the first impact point is in the center of the ceramic plate,the penetration probability of the second impact is 1.04%.When the first impact point is in the center of the ceramic plate,and the second impact point is in the horizontal adjacent center,the four corners of the gap,or the diagonal adjacent center,the penetration probability of the third impact on the ballistic plate is 5.45%,7.35%,and 5.05%,respectively.This method can be used to quickly evaluate the resistance of damaged equipment to multiple penetrations.

Laser-guided weapons play a crucial role in modern warfare.The missiles and other weapons are directed to accurately strike targets by using the precise guidance information obtained through lasers，making them one of the key factors that influence the course of the battle and determine victory or defeat.This paper summarizes the typical semi-active laser-guided weapons，including various types of laser-guided bombs and missiles，multi-mode composite guided weapons with semi-active laser guidance，target laser designators，in the world and the current development status of anti-jamming methods.On this basis，the principles and technological core of semi-active laser-guided weapons are analyzed in depth.In response to the existing technical challenges and development obstacles，it is proposed that the future semi-active laser-guided weapons will evolve towards multi-mode composite，multi-jamming integration，multi-protection combination，as well as miniaturization，generalization，and multi-functionality.

Addressing challenges in drone swarm damage assessment—such as unclear component-level damage mechanisms and insufficient understanding of formation configuration effects—this study proposes an evaluation method integrating high-fidelity component-level damage modeling with formation dynamics analysis. By establishing a damage calculation chain of “physical damage → component failure → functional damage” for quadrotor drones, the structure-effect relationship of target vulnerability is analyzed. Combined with a fragmentation-explosive warhead blast field model, this quantifies the dynamic impact of different damage elements on drone functionality. Furthermore, considering four typical swarm formations (one character, V character, snake, round), multi-scenario simulations were conducted using the standard damage percentage criterion and warhead-target encounter conditions. Results indicate that: For single-drone targets, the number of effective fragment hits negatively correlates with burst distance; Blast waves demonstrate superior damage efficacy at close ranges, with detonation below the drone yielding optimal results; Swarm damage outcomes are influenced by both detonation position and formation type, with formation being the dominant factor—circular formations sustain the highest damage, while serpentine formations exhibit the lowest. This research provides methodological support for damage assessment of fragmentation warheads against drone swarms and offers theoretical insights for tactical formation selection.

This research has significant practical significance for enhancing defense strategies and has attracted significant attention from scholars worldwide. There is a lack of systematic research on the calculation methods and models for digital human fragment injuries based on experimental wound ballistics in China. A construction method for digital human fragment injury model is proposed, and a high-precision human geometric model is established. The injury calculation models are derived from the fragment penetration experiment and numerical simulation. The quantitative criteria for the penetrating depth and aperture of fragment into the human tissues and organs, as well as personnel injury are established. A digital human fragment injury calculation software is developed using Qt platform and OpenGL technology, and the representative cases of single fragment hitting a specific part of human body and hitting the human body at different angles are studied. The digital human fragment injury calculation method and the associated high-precision model software are proposed to offer scientific support for battlefield fragment injury assessment, casualty prediction during warhead attacks, and personnel protection strategies.

To address the challenges of extreme scale variations，dense occlusion of small targets，and complex background interference in unmanned aerial vehicle （UAV）-based target detection，this paper proposes a cross-layer dynamic detection network based on an improved YOLOv10 for the detection of small target via UAV aerial photography.A dual-branch cross-layer feature fusion pyramid network for replacing the original pyramid network is designed to resolve the problem of insufficient detail preservation for small targets in traditional methods.A channel-shuffling depth-wise upsampling module is developed，which combines channel shuffle operations with depth-wise separable convolutions and enhances the edge features of small targets through high-frequency residual connections.An end-to-end dynamic detection head is adopted to replace the original detection head，and a dynamic weighting mechanism is introduced，which enables the adaptive adjustment of feature representations at each position based on contextual information.Experimental results show that the proposed detection network achieves mAP0.5 of 53.3% and mAP0.5：0.95 of 33.2% on the VisDrone 2019 validation set，which are improveed by 12.7% and 9% ，respectively，compared to YOLOv10s，while reduces the model parameters by 23.7% and achieves an FPS of 79.The proposed algorithm significantly enhances the detection accuracy while maintaining excellent inference speed.

In complex geographical environments such as uncertain area，hostile environment and restricted area on the battlefield，conducting the search and rescue operation for persons in distress faces numerous challenges including low efficiency and poor positioning accuracy.How to utilize emerging equipment such as unmanned aerial vehicles （UAVs） to improve the efficiency of search and rescue operations for the persons in distress has become a research hotspot across the world.A searching and positioning UAV micro-swarm system composed of three multi-rotor UAVs is designed.This system is mainly composed of quadcopter UAVs，electro-optical search payloads，broadband ad hoc network radios，and UAV ground stations，etc.The day and night search and collaboration control are realized by single UAV and muliti-UAV collaboration.The miniaturized design of UAV makes it easy for the operator to carry with a backpack，facilitating forward reconnaissance to determine the locations information and the condition of the injury.The system can precisely locate the persons in distress and has the advantages of being less restricted by terrain and environment and having high search efficiency in the area.UAVs can achieve multi-mode networking communication among multiple unmanned platforms，and the ground control station has the functions of mission planning，setting the mission routes for multiple UAVs，and controlling UAVs to fly in formation，which can enhance the efficiency of battlefield search and rescue through the multi-UAVs collaboration.

Magnetic field-modulated fractional-slot permanent magnet motor utilizes the magnetic field modulation effect to eliminate the need for the gearbox structure in traditional electric propulsion system，which simplifies the electric propulsion system architecture and reduces mechanical noise.Furthermore，magnetic field -modulated fractional-slot permanent magnet motor is particularly suitable for the electric propulsion of AUVs due to their high power density and high torque density.Magnetic field -modulated fractional-slot permanent magnet motor can utilize multiple harmonic components of the excitation magnetic flux density to work cooperatively，significantly enhancing the motor’s torque density.However，this type of motor simultaneously modulates certain low-order harmonic components of the armature magnetic flux density.This leads to the issues such as low power factor，high harmonic losses，and significant torque ripple in magnetic field-modulated motors，negatively impacting the stealth capabilities and operational efficiency of AUVs.This paper focuses on the control strategies for torque ripple suppression，power factor improvement，and efficiency optimization from the perspective of motor optimization control.Finally，the current state of technology regarding the application of such motors in new energy power generation is summarized，marine propulsion，and electric vehicles，followed by a discussion on the key technologies and future development directions for their application in AUVs.

It is required to fastly and accurately predict the exterior ballistics and impact points of mortars on the modern battlefield.A mortar exterior ballistics prediction method based on Transformer-long short-term memory (Transformer-LSTM) hybrid neural network is proposed.The Transformer network is utilized to extract the intrinsic joint features of mortar’s velocity and three-dimensional coordinates at the moments from T to T+K,and the LSTM network takes these time series features as an input to map the three-dimensional coordinate information at the moment of T+K+1.In order to optimize the network model,the effects of different sliding window step sizes on the convergence performance of the exterior ballistics prediction model are investigated and analyzed.The proposed hybrid network is compared with GRU and LSTM networks in terms of single-step,multi-step and impact-point prediction.It is found that the prediction accuracies of the proposed hybrid network for the three-dimensional coordinates of exterior ballistics can reach up to 99.78%,99.72% and 99.81%,which are better than those of GRU and LSTM networks; and the single-step prediction of the exterior ballistics of the proposed hybrid network consumes only 1.2ms,which significantly improves the prediction accuracy and efficiency.The method enables accurate and fast prediction of exterior ballistics and impact point,providing more response time for mortar interception missions.

The operational safety and catalytic efficiency of hail-suppression and rain-enhancement shells,as critical tools for weather modification,are directly affected by the fragmentation characteristics formed after self-destruction.This paper investigates the mechanism of shell material mechanical properties effecting on fragment formation through experiments,theories and numerical simulations.The differences in fragment mass distribution,morphological characteristics and energy dissipation among S20 steel,9260 steel,D60 steel,and 823 steel are analyzed.The results reveal that the strength-toughness synergy of shell materials plays a decisive role in fracture patterns.High-strength materials (e.g.,9260 steel) generate uniform fine fragments (>60% of 1-5g fragments) but exhibit localized energy concentration due to insufficient toughness.Low-strength high-toughness materials (e.g.,D60 steel) produce irregular agglomerated fragments (32% of more than 10g fragments) due to significant plastic deformation.In contrast,823 steel demonstrates optimal brittle-ductile fracture coordination under explosive loading due to its unique mechanical properties (1000-1200MPa tensile strength and 40-60 J impact toughness),with 85% of less than 5g fragments and over 90% of ≤10g fine fragments,which fully complies with national safety standards (Class B,≤10g) and significantly reduces ground safety risks.The research provides theoretical guidance for optimizing the hail-suppression and rain-enhancement shell materials and holds substantial engineering value for enhancing the weather modification safety.

The influence of aluminum powder content on the energy output characteristics of RDX-based aluminized explosives is studied.The free-field static explosion tests are conducted on aluminized explosives with aluminum powder contents of 20%,30%,and 40%.The overpressure-time curves of free-field and ground shock waves are measured using overpressure sensors,and the propagation processes of incident waves,reflected waves,and Mach waves,as well as the changes in the explosion fireball are captured using a high-speed camera.The JWL-Miller parameters of the aluminized explosives are calibrated based on the test results.The test results show that the peak overpressure and specific impulse of ground reflected wave of the aluminized explosive are slightly affected when the aluminum powder content increases from 20% to 30%.When the aluminum powder content increases from 30% to 40%,the peak overpressure of ground reflected wave is decreased by 2.17% to 7.81%,and the specific impulse is decreased by 0.29% to 12.17%.When the aluminum powder content increases from 20% to 40%,the maximum diameter of explosion fireball increases from 6.48m to 7.12m,and the time when the explosion fireball begins to shrink increases from 20ms to 60ms.The maximum errors of the blast shock wave parameters (peak overpressure and specific impulse) of 30% and 40% aluminized explosives predicted using the JWL-Miller parameters are 16.75% and -8.51%,respectively,compared with the test results,and the average absolute percentage errors are 6.76% and 4.14%,respectively.This study can provide reference and guidance for the prediction of the power field of aluminized explosives.

For the task assignment of loitering munition swarm cooperatively conducting the reconnaissance，attack and assessment （RAA） tasks，a joint-bidding-based consensus-based bundle algorithm （JCBBA） is proposed by taking into account the constraint of time precedence，the heterogeneous characteristics of target property and the requirement of supporting the multiple munitions to jointly execute an identical task.Munition swarm task assignment of cooperative RAA is first formulated as combinatorial optimization problems.Then based on the structure of CBBA，a task bundle construction mechanism，an individual biding strategy and a marginal benefits computation method considering joint biding are tailored to achieve the fast and robust assignment of task with time precedence constraint in non-center communication networks.Numerical simulations under different scenarios show that JCBBA can achieve reasonable allocation of time precedence tasks while multiple constraints are satisfied，and the comparison tests demonstrate that JCBBA could better trade-off the computation efficiency and optimality with the solving time reduced by approximately 40% compared to consensus-based coalition algorithm.

In order to study the explosion driving characteristics of shell fragments driven by cylindrical charge with cavity and reveal the mechanism of the influence of shaped charge structure on the initial velocity of shell fragments,the explosion driving process and initial velocity distribution of shell fragments under different cavity cone angles are studied through the numerical simulation.The initial velocity distribution function of shell fragment along the axial direction is fitted by analyzing the rarefaction wave propagation path,velocity loss coefficient and fragment acceleration process.The relative error between the calculated and simulated results is less than 15%.The results show that the earlier generation of rarefaction wave and the decrease in cross-section ratio of mass of charge to shell are the main factors contributing to the decrease in the initial velocity of fragments.The initial velocity of the fragments near the non-detonation end decreases with the decrease in the cone angle of cavity and the increase in the cone top height of cavity.The rarefaction wave reflected from the non-detonation end is an approximately planar wave and is at a certain angle to the cavity surface.The instant when rarefaction wave reaches shell is linearly and positively correlated with the axial position of the shell.The cross-section ratio of mass of charge to shell has a quadratic function relationship with k,where k is the ratio of the distance from the cross section to the non-initiating end to the height of cavity cone top.The velocity loss coefficient decays exponentially with the decrease in k.The research can provide a useful reference for the overall structural design,material selection and structural optimization of anti-armour and anti-personnel composite warheads and the deformable fragment warhead with cavity charge structure.

To address the challenges of multispectral feature mismatch，complex scene interference，and insufficient detection accuracy in drone aerial vehicle target detection，this paper proposes Multi-Scale Gated Fusion Network（MSGF-Net），a YOLOv10-based multi-feature space joint optimization network.First，the network employs a dual-stream feature extraction network and introduces a gated local-global fusion （GLGF） module into the backbone.This enables effective interaction and joint optimization of visible and infrared features across mult-feature spaces，thereby mitigating the impact of feature mismatch and enhancing feature representation.Subsequently，after the feature pyramid network，MSGF-Net incorporates a cross-modulation block （CMB） module to perform the pixel-wise weighted fusion of multi-feature space information，further improving the complementarity between different spectral features.Experiments on the public DroneVehicle dataset demonstrate that MSGF-Net achieves 83.4% mAP_0.5and 63.9% mAP_0.5：0.95，showing a significant improvement compared to the single-channel model YOLOv10n.Furthermore，compared to leading multi-modal fusion algorithms like C2Former and TSDADet，MSGF-Net increases the mAP_0.5：0.95 by over 9 percentage points，providing compelling evidence of its superior accuracy and robustness.

To address the limitations of existing traversability analysis methods,which often suffer from incomplete obstacle recognition and poor generalization in complex off-road environments,this paper proposes an obstacle recognition method based on open-vocabulary semantic segmentation.The method extracts the semantic labels of obstacles and terrain around the vehicle,enabling effective identification of previously unseen obstacles in unstructured environments.The method is validated on the datasets in real-world experiments,demonstrating its stability and comprehensive recognition capability.On this basis,a multi-layer 2.5D map is constructed by integrating semantic labels with 3D point clouds.The traversability level of a terrain is preliminarily classified according to semantic labels.And then the terrain smoothness is quantified based on ground elevation,and the geometric parameters of special environmental features (e.g.,vertical wall) are measured.Furthermore,the driving posture of vehicle is predicted by incorporating the geometric configuration of a tracked vehicle,thereby quantifying the coupling relationship among static slope stability,semantic terrain categories and geometric attributes.A cost function is then designed to jointly assess the traversal risk and cost of vehicle,ultimately generating a vehicle-centric traversability map.The effectiveness and reliability of the proposed method are verified by comparing it with similar methods,which enhances data support for decision-making,planning,and control of unmanned tracked platforms.

The perforated array ceramic plate is a new type of protective armor. The projectiles are deflected by the asymmetric forces caused by the holes in the plates, reducing the stability and penetration ability of the projectiles. In this paper, FEM-SPH adaptive algorithm was used to model the penetration of a 7.62mm armor-piercing incendiary projectile into a perforated array boron carbide ceramic target plate. The effects of impact points, angles, and projectile rotation on the ballistic performance of the perforated array ceramic plate were analyzed. It is shown that the impact points have significant influence on the projectile-target interaction, the failure mode and the ballistic performance of the perforated array ceramic plate. The active failure mode of the projectile for the impact point located at the hole edge is deflected due to asymmetric forces, resulting in the enhancement of the ballistic performance of the plate. For the oblique impact, the projectile stability is further reduced by the plate, giving rise to the enhancement of the ballistic performance and expansion of the damage area on the plate. Comparing to non-rotating projectiles, the plate has greater advantages against the rotating projectiles.

Aircraft aerodynamic configuration design has long adhered to an experience-driven design philosophy,where the aerodynamic performance of the baseline shape often determines and limits the performance improvements achievable through shape optimization.This traditional design approach is incapable of yielding disruptive aerodynamic configurations.In recent years,with the emergence of aerodynamic topology design concepts,aerodynamic topology optimization methods have gained attention and are expected to be applied to the design of novel aircraft aerodynamic configurations.Focusing on aerodynamic topology optimization methods,this paper reviews their development in terms of topology representation methods and optimization algorithms,and analyzes typical application cases of existing methods in the design of novel aircraft aerodynamic configurations.First,the historical development of aerodynamic topology optimization methods is outlined.Second,recent advances in the application of aerodynamic topology to novel aircraft configuration design are summarized.Subsequently,the challenges and difficulties faced by aerodynamic topology optimization methods are discussed,including difficulties in handling high-speed compressible turbulent flows and insufficient smoothness at fluid-solid interfaces in aircraft flow topology.Finally,future research directions in this field are prospected.

Infrared small target detection has extensive applications in military fields such as infrared guidance and tracking systems and is an important area of infrared image processing.Due to the limitations in detection equipment and the lack of inherent information about infrared small targets,the existing detection methods are difficult to meet the practical performance requirements.In order to explore a lightweight and highly accurate infrared small target detection model,a lightweight and efficient YOLOv10n infrared small target detection model (L-YOLOv10n) is designed based on YOLOv10n.The SCDown module in YOLOv10n is replaced by a lightweight spatial-channel decoupled downsampling (L-SCDown) module to enhance the key features of infrared small targets with a low computational cost.A lightweight Cross-stage partial convolution with Two Fusion layers (L-C2f) module is used to replace the C2f module,thereby enhancing the edge information of small targets and extracting the multi-scale features while reducing computational cost.To address the issues of infrared small targets with few pixels and an imbalance between foreground and background,Focal Loss and a focaler intersection-over-union (Focaler-IOU) loss function are introduced,thus allowing the model to better focus on the difficult-to-detect targets.Experimental results on the public datasets SIRST-V2 and NUDT-SIRST demonstrate that L-YOLOv10n significantly outperforms the detection-based models in both detection performance and resource consumption.The detection performance of L-YOLOv10n is slightly lower than that of Transformer-based segmentation models,but its resource consumption is significantly better than those of other models.Its generalization performance on the NUDT-SIRST dataset is also significantly higher than those of most infrared small target detection models.These results demonstrate that the proposed model strikes a balance between resource consumption and high-precision detection,demonstrating its practicality.

The negative obstacles pose a significant threat to the driving safety of unmanned ground vehicles (UGVs) when they operate in unstructured environments.However,in the existing research on negative obstacle detection,the camera-based detection methods are not well suited for the detection of negative obstacles in unstructured environments with poor lighting conditions and complex backgrounds; and LiDAR(light detection and ranging)-based detection methods are mostly designed based on the characteristics of mechanical LiDAR harnesses and have poor universality.Therefore,a highly compatible and scalable negative obstacle detection method is proposed,which can adapt to different types of LiDAR and achieve high-precision negative obstacle detection.The proposed method involves preprocessing the point cloud data,extracts the ground regions of interest,and performs point cloud pose correction.The adaptive resolution polar coordinate rasterization technology is used to enhance the spatial representation capability of point cloud data.A negative obstacle grid feature descriptor is designed,and the potential negative obstacle regions are extracted from multiple features such as the hollow characteristics,height differences,and minimum height of point clouds.A multi-frame fusion strategy is introduced,and the high-precision negative obstacle surface occupancy range is outputed through map reprojection and Bayesian rule-based probability updates.The experimental results show that the proposed method is applicable to LiDARs with different scanning modes,and can effectively identify the negative obstacle areas in complex unstructured environments.

Proportional servo valves are the core components in defense heavy-duty hydraulic drive systems such as missile launcher,tank and artillery.Due to the fact that the spool motion system is constrained by the complex dynamic characteristics such as hysteresis,flow force and Stribeck friction,the conventional proportional integral derivative (PID) control method is unable to overcome the inherent performance limitations.An adaptive active disturbance rejection controller (AADRC) for electro-hydraulic proportional servo valves is proposed,which integrates a parameter adaptation law with an extended state observer (ESO).A state-space model of the position loop for spool motion is established,revealing its nonlinear dynamic coupling mechanisms.To address the parameter uncertainties such as hydraulic valve spring stiffness and viscous damping coefficient,a parameter adaptation law is designed for online estimation of unknown parameters,and an ESO is utilized to compensate dynamically for the coupled nonlinear disturbances arising from hysteresis,flow force and unmodeled friction.Based on Lyapunov stability theory,it is rigorously proved that the controller is used to guarantee the global uniform ultimate boundedness (GUUB) of the closed-loop system’s states,parameter estimation errors and disturbance observation errors.Experimental results demonstrate that the proposed AADRC exhibits the smallest steady-state tracking error under both low-speed (0.5Hz) and high-speed (2Hz) sinusoidal conditions compared to PID controller and nominal model-based robust controller,and effectively overcomes the lag during reversing in triangular wave conditions.

A polynomial guidance law that takes into account the constraints on impact angle，impact speed and seeker’s field-of-view （FOV） is proposed for the issue of air-to-ground missile attacking the moving ground target in a two-dimensional scenario.Based on polynomial shaping theory，the line-of-sight （LOS） angle between the missile and the target is formulated as a polynomial function of the relative distance.An algebraic relationship for the polynomial coefficients is derived by using the boundary conditions such as the impact angle constraint.To address the impact speed constraint，the unknown polynomial coefficient is designed as a control parameter and is updated using an iterative learning method，thereby generating a nominal guidance command.Considering the limitation imposed by the FOV angle constraint of seeker，a safe set is established using the control barrier function method.An optimization problem is formulated to adjust the nominal guidance command，ensuring that the seeker’s FOV remains within the constrained boundary throughout the engagement.Numerical simulations demonstrate that the proposed guidance law can be used to guide the missile to the target while satisfying the constraints on impact angle，impact speed and seeker’s FOV.

To solve the problems existing in the modular design of gun equipment,such as the insufficient overall of common modules and the lack of adaptability evaluation,a design method of modular firepower system for multi-platform universal artillery is proposed.Taking a large-caliber artillery as the research object,A modular design process based on launch dynamics is established using the parametric modeling and multi-level optimization algorithm,and the corresponding fitness evaluation method and tool are developed.The fitness evaluation tool is used to evaluate the universal design of firepower system on a 4 types of chassis.The results show that the tracked chassis can be fully adapted; for the wheeled chassis,the loading amount needs to be reduced; for the truck chassis,the position of seat ring needs to be adjusted; and for the amphibious chassis,the weight needs to be reduced by more than 55%.The design process and evaluation method significantly improve the simulation and optimization design efficiency of gun equipment,and can be directly applied to the similar problems of more design variables and working conditions,providing a strong technical support for the modular design and optimization of weapon equipment.

Ballistic helmets are critical protective equipment designed to mitigate craniocerebral injuries caused by ballistic impacts and blast shocks.The cushioning system within the helmet plays a vital role in attenuating the damage effects from such impacts.To enhance the overall protective performance of headgear,a novel cushioning material—EVA/SSG composite foam—was developed by incorporating Shear Stiffening Gel (SSG) into ethylene-vinyl acetate (EVA) foam.Using a ballistic impact testing platform and a synthetic head model,comparative ballistic tests were conducted on helmets equipped with conventional EVA pads and those with the EVA/SSG pads.The temporal variations in impact force on the headform surface and the acceleration at the centroid of the head model during impact were investigated.Additionally,finite element simulations of the ballistic impact were performed.Both experimental and numerical results demonstrate that,under ballistic impact conditions,the helmet with EVA/SSG foam liners reduces the peak pressure on the headform surface and the peak acceleration at the centroid by more than 20%,thereby significantly lowering the probability of head injury.

To address the limitations of traditional penetration efficiency prediction models, such as their reliance on empirical formulas, limited adaptability, and weak physical interpretability, this study proposes a modeling approach that integrates dimensional analysis with symbolic regression. A predictive model is developed for the penetration efficiency of rod projectiles impacting semi-infinite metal targets. Based on physical prior knowledge, seven original physical variables are transformed into four dimensionless control parameters with clear physical significance. Using 819 sets of experimental data, an analytical expression between the dimensionless parameters and penetration efficiency is constructed through a symbolic regression algorithm based on genetic programming, with a penalty mechanism introduced to ensure the participation of all control variables. The Results show that the proposed model performs well across various penetration conditions, with average coefficients of determination (R²) exceeding 0.8. The model accurately captures the nonlinear influence of each parameter on penetration efficiency. Compared to traditional empirical models, the proposed method offers improved predictive accuracy and adaptability, while producing physically meaningful and structurally clear expressions that provide insight into the role of key variables in the penetration process.

This study addresses the critical challenges in risk assessment of natural fragmentation warhead explosions by proposing a comprehensive parametric analytical methodology. Through systematic integration of key components including stochastic fragment generation, precise trajectory calculation, three-dimensional_h probability evaluation, and quantitative human damage assessment, we have established a multi-scale coupled risk evaluation system. In terms of fragment kinematics modeling, we developed an aerodynamic surrogate model based on artificial neural networks. By introducing fragment sphericity parameters and Mach number as dual variables, the model significantly improves the trajectory calculation accuracy for naturally fragmenting projectiles under random tumbling conditions. For hazard effect evaluation, we innovatively proposed a three-dimensional pie-shaped target model. By incorporating detailed human geometric parameters and considering the relative position between fragment trajectories and human targets, the model enables more accurate calculation of fragment hit probability on personnel. Regarding risk quantification, we constructed a multi-level probabilistic assessment framework that integrates AIS injury scales with fragment kinetic energy distribution. Validation using 155mm projectiles demonstrates that this method can not only describe fragment hazards through uniform annular safety distances but also generate two-dimensional spatial distributions of fragment hazard probability. The research achieves quantitative characterization of the entire process encompassing initial stochasticity, motion complexity, and progressive damage effects of fragments. It provides new analytical tools and decision support for dynamic safety distance determination in ammunition storage and transportation, optimized safety zoning design for firing ranges, and industrial explosion protection.

Addressing the issue of low recognition rates for complex multi-class radar signal modulation types under low signal-to-noise ratio,a radar signal modulation recognition method based on time-frequency fusion features and multi-scale dual attention network is proposed.By applying three time-frequency analysis methods,namely,smoothed pseudo Wigner-Ville distribution,Fourier synchrosqueezed transform and Hilbert-Huang transform based on variational modal decomposition,and the denoising preprocessing technique,the radar signals are transformed into three-channel time-frequency feature maps,which significantly enhances the robustness and expressive power of the features.A multi-scale dual attention network is designed to realize denoising preprocessing technique the cross-scale information fusion and noise suppression through the multi-scale channel attention mechanism.The time-frequency structure of radar signals is adaptively perceived by utilizing the multi-scale spatial attention,and the information is further integrated through gated fusion and residual connection.The experimental results show that the method achieves an average recognition rate of 98.99% for 12 types of typical radar signal modulation modes under the condition of a signal-to-noise ratio of -10dB,showing good robustness.

This paper proposes an adaptive key edge detection framework based on deep reinforcement learning to address the challenge of identifying the key edges in space-based kill chain network.This method first uses complex networks to model a space-based kill chain.And then a template-based kill chain search method is proposed by introducing the idea of backtracking search.A key edge exploration paradigm is constructed based on deep reinforcement learning,which combines a layered experience replay mechanism with a dynamic ε-greedy strategy.,The precise localization of key edges is achieved through multi-dimensional state representation.The experiment shows that,in the simulation of space-based kill chain network testing,the Top-10 recognition accuracy of the proposed method reaches 85%,which is 89.5% higher than that of the traditional betweenness centrality method and 19.7% higher than the benchmark of deep Q-network (DQN).In terms of network robustness indicators,the global efficiency reduction rate η is 37.4% higher than the edge betweenness centrality,and the prediction error of the maximum connected component retention rate is controlled within 5%.

Explosive shock waves interacting with typical materials at different angles of incidence can lead to severe reflected overpressure, posing significant threats to structures and personnel. However, the accuracy of existing reflected pressure prediction models remains limited. This study develops a theoretical model based on mass and momentum conservation equations, combined with the equations of state for air, granite, and 45# steel. A series of explosion experiments at different charge heights were conducted to measure both free-field and reflected pressures. Theoretical results were compared with experimental data, showing strong agreement. The proposed model effectively predicts reflected overpressure across a range of incidence angles and material types, offering valuable support for shock wave damage assessment and protection design.

The afterburning reaction mechanisms of aluminum nanoparticles (ANPs) in the detonation product atmosphere of aluminized explosives are studied using the ReaxFF-lg reactive force field alongside reactive molecular dynamics (MD) simulations. The combustion process of a 10nm ANP is simulated in a high-temperature and high-pressure environment (2500~3500K) containing the principal detonation products of CL-20 (CO₂, H₂O, CO, and N₂), revealing the reaction mechanism of ANP in a multi-component oxidizing atmosphere at the atomic scale. Results indicate that the formation of H—Al and H—C bonds decreases as the temperature rises, which indicates that the elevated temperatures are not conducive to the creation of hydrogen-related stable structures. Additionally, the proportion of CO₂ among detonation products emerges as the primary determinant of detonation temperature. CO₂ demonstrates greater reactivity than H₂O and plays a pivotal role in promoting the oxidation of aluminum oxidation and the release of energy within the range of 2500~3500K. Thus, increasing the content of CO₂ in detonation products can effectively regulate detonation temperature and boost the combustion efficiency of ANPs, enabling more complete energy release. These findings offer a theoretical foundation for designing high-performance aluminized explosive formulations.

Based on the Smoothed Particle Hydrodynamics (SPH) method, a parallel numerical simulation method was developed for projectile penetration into thin metal targets to address complex physical phenomena such as large deformation, damage, and fracture involved in high-velocity penetration. To accurately describe the material’s mechanical response under high-speed loading, a simplified Johnson-Cook damage model was adopted. To tackle the high computational cost caused by the rapid increase in particle numbers in SPH simulations, the computational domain was divided into subdomains, and an MPI-based CPU parallel solver was developed and implemented to exchange particle information between them. The accuracy of the numerical method in predicting residual velocity and the penetration process was validated by comparing the simulation results with experimental data from the literature. For the 8mm and 10mm thick target plates, the maximum prediction errors were 7.86% and 5.44%, respectively. A systematic evaluation of the parallel framework's acceleration performance showed a significant improvement in computational efficiency. For a medium-scale problem involving approximately 1.79 million particles, a parallel speedup efficiency of 0.76 was achieved using 54 CPU cores. While ensuring simulation accuracy, this parallel SPH framework successfully extends the computational capability to a higher order of magnitude, enabling large-scale simulations with over 100 million particles.

A cooperative encirclement interception method based on virtual aiming points is proposed for intercepting the high-speed maneuvering targets in near-space in three-dimensional scenarios.A calculation method for the ideal interception time of interceptor groups is derived by analyzing the maneuvering envelope of target and considering the guidance characteristics of the interceptors.Thus,the problem of intercepting the high-speed maneuvering targets is transformed into a problem of hitting a specific point on a fixed spatial plane at a predetermined time and angle.Based on the ideal interception time and angle of attack,the virtual aiming points are selected in space to guide the interceptors,ensuring that the impact angles meet the head-on interception conditions,thereby reducing the requirement for the speed of interceptor.The interception tests are conducted on two typical C-type and S-type maneuvering targets through numerical simulation.The results demonstrate that the proposed method is used to successfully intercept C-type and S-type maneuvering targets with different maneuvering intensities,achieve a minimum group miss distance of less than 3 meters,and enable the impact angle meet the requirements of head-on interception angle.

To evaluate the performance of tracked vehicles,a multiple road surfaces-three-dimensional driving cycle construction method based on micro-motion segments is proposed.This method aims to address the issues of various types of road surfaces,longer short-trip segments,and the numerous dimensions of influencing factors in the construction of driving cycles for tracked vehicles.The collected driving data of tracked vehicles is processed,and the three-dimensional data such as speed,angular velocity,and ground resistance coefficient are extracted.The K-means clustering method is used to categorize the driving segments into three typical road surfaces:paved road,gravel road,and undulating dirt road.The short-trip segments are divided into micro-motion segments based on their minimum weighted three-dimensional variation rate,and the feature extraction and clustering analysis are made for the short-trip segments.A three-dimensional driving cycle is constructed using the Markov transition probability of the micro-motion segments,and a corresponding comprehensive evaluation system is proposed.The total duration of the constructed driving cycle is approximately 2000 seconds,and the average feature coverage rate of three types of road surfaces is up to 94.63%.This driving cycle accurately reflects the driving characteristics of tracked vehicles and serves as an effective tool for simulation and bench testing of tracked vehicles.

Since the concept of kill chain was proposed，the kill chain in various combat domains，especially the aerial kill chain，has played an increasingly important role in the informationized and intelligent battlefield.The construction of the aerial kill chain includes link design，link connectivity，and link verification，which requires the coordination of various human and material resources to be completed.It has strong system characteristics and engineering practicality.However，most of the existing researches related to aerial kill chain remain at the theoretical level，and the verification methods are mostly theoretical derivation or simulation deduction，which are unable to effectively guide the practical construction of aerial kill chain.In view of this，an aerial kill chain construction method is proposed based on the system engineering principle and practical experience.The SWOT analysis method is used to study and analyze the aerial kill chain and refine the basic principles of link construction.A Hall three-dimensional structural model of the aerial kill chain is established to clarify the basic logic of link construction.And then an interpretative structural model is used to design the aerial kill chain to clearly reflect the structural characteristics of the system.The case verification is made on the proposed aerial kill chain construction method，and an information transmission network is established to connect the equipment nodes.The orthogonal experiment and evaluation index system are designed.The results such as link closure time，link availability，node matching and target damage degree are obtained through experiment.It is proved that the aerial kill chain construction method based on system engineering principle can effectively and reasonably guide the construction practice of aerial kill chain.

Transformer is a kind of mainstream method used for infrared and visible image fusion (IVIF).However,most of the Transformer-based methods for IVIF suffer from the problems such as large number of parameters and high computational complexity.To this end,a method for infrared and visible image fusion based on Mamba-empowered triple-branch generative adversarial network is proposed.Specifically,three independent and cooperative branches for feature extraction and fusion are designed in the generator network.The infrared and visible branches are utilized to extract the global features from the infrared and visible images,respectively,through the shallow feature extraction modules and Mamba blocks.Meanwhile,the global features extracted by the infrared and visible branches are hierarchically integrated into the convolutional neural network-based intermediate fusion branch,thereby achieving the full interaction and fusion between the local and global features.Furthermore,the generator is competitively trained against two discriminators (infrared discriminator and visible discriminator),forcing the generator to improve its ability to produce fusion image.The experiments on public datasets indicate that the proposed method outperforms other methods both in the qualitative visual effects and the quantitative objective metrics.

Erosion wear and fatigue cracks are the key factors causing the failure of gun barrel.Erosion wear leads to a decrease in shooting accuracy，while the propagation of fatigue cracks can weaken the barrel strength and potentially lead to catastrophic explosion，posing a potential threat to the safety of gun launches.For this purpose，a construction scheme for a digital twin model of gun barrel based on fatigue fracture simulation is proposed.This involves developing the high-fidelity geometric and finite element models of gun barrel and comprehensively considering the effects of the high-temperature，high-pressure impact loads and their coupled interactions，as well as the structural and manufacturing factors such as coatings and preload forces.A digital twin simulation model for gun barrel is constructed based on elucidating the propagation behavior of micro-cracks on the inner wall of gun barrel.Building upon the health monitoring and remaining service life evaluation method for gun barrels based on outer wall strain measurements，the real-time online detection of damage parameters for crack propagation in gun barrels is achieved.This scheme lays the foundation for constructing an interactive digital twin model of gun barrels and provides a new technical path for preventing the barrel burst and achieving the dynamic prediction of barrel life.Finally，the key technologies for constructing the digital twin model of gun barrels atr proposed，which can serve as a valuable reference for digital twin research on other barrel-type weapon systems.

Rolling bearing is a key component in a large number of rotating machines，and its remaining useful life (RUL) prediction is related to whether an equipment can operate safely and stably.In order to solve the current problem of low accuracy of RUL prediction，a prediction method combining temporal convolutional autoencoder (TCAE) and Informer (TCAE-Informer) network in frequency domain is proposed for RUL of rolling bearings.The method designs a TCAE for the frequency-domain signals of different time samples of rolling bearings，and adaptively extracts the depth features that better reflect the full-life degradation law of rolling bearings.An Informer network model is built，and with the help of its learning advantage in long sequence information，the mapping relationship between the depth features and the RUL of rolling bearings is effectively fitted to realize the RUL prediction function of rolling bearings.XJTU-SY bearing dataset is used to compare and validate the evaluation indexes of three RUL prediction results.The results show that the proposed method is able to achieve more accurate RUL prediction effect under different working conditions compared with many existing methods，which proves that it has superior RUL prediction ability and generalization ability.The anti-interference test is carried out for different methods.In the test，the proposed method shows better RUL prediction effect under different noise conditions，which proves that it has superior anti-interference ability of RUL prediction.