Defence Technology(ISSN 2214-9147(O);2096-3459(P)),sponsored by China Ordnance Society,is published monthly and aims to become one of the well-known comprehensive journals in the world,which reports on the breakthroug...Defence Technology(ISSN 2214-9147(O);2096-3459(P)),sponsored by China Ordnance Society,is published monthly and aims to become one of the well-known comprehensive journals in the world,which reports on the breakthroughs in defence technology by building up an international academic exchange platform for the defence technology related research.It publishes original research papers having direct bearing on defence,with a balanced coverage on analytical,experimental,numerical simulation and applied investigations.It covers various disciplines of science,technology and engineering.展开更多
Military image encryption plays a vital role in ensuring the secure transmission of sensitive visual information from unauthorized access.This paper proposes a new Tri-independent keying method for encrypting military...Military image encryption plays a vital role in ensuring the secure transmission of sensitive visual information from unauthorized access.This paper proposes a new Tri-independent keying method for encrypting military images.The proposed encryption method is based on multilevel security stages of pixel-level scrambling,bitlevel manipulation,and block-level shuffling operations.For having a vast key space,the input password is hashed by the Secure Hash Algorithm 256-bit(SHA-256)for generating independently deterministic keys used in the multilevel stages.A piecewise pixel-level scrambling function is introduced to perform a dual flipping process controlled with an adaptive key for obscuring the spatial relationships between the adjacent pixels.Adynamicmasking scheme is presented for conducting a bit-level manipulation based on distinct keys that change over image regions,providing completely different encryption results on identical regions.To handle the global correlation between large-scale patterns,a chaotic index-map system is employed for shuffling image regions randomly across the image domain based on a logistic map seeded with a private key.Experimental results on a dataset of military images show the effectiveness of the proposed encryption method in producing excellent quantitative and qualitative results.The proposed method obtains uniform histogram distributions,high entropy values around the ideal(≈8 bits),Number of Pixel Change Rate(NPCR)values above 99.5%,and low Peak Signal-to-Noise Ratio(PSNR)over all encrypted images.This validates the robustness of the proposed method against cryptanalytic attacks,verifying its ability to serve as a practical basis for secure image transmission in defense systems.展开更多
Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation ...Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation plan is a key step in the organizational planning.Based on the description of the problem and the definition of organizational elements,the matching model of platform-target attack wave is constructed to minimize the redundancy of command and decision-making capability,resource capability and the number of platforms used.Based on the non-dominated sorting genetic algorithmⅢ(NSGA-Ⅲ)framework,which includes encoding/decoding method and constraint handling method,the generation model of organizational force formation plan is solved,and the effectiveness and superiority of the algorithm are verified by simulation experiments.展开更多
In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,...In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.展开更多
With the continuous advancement of unmanned technology in various application domains,the development and deployment of blind-spot-free panoramic video systems have gained increasing importance.Such systems are partic...With the continuous advancement of unmanned technology in various application domains,the development and deployment of blind-spot-free panoramic video systems have gained increasing importance.Such systems are particularly critical in battlefield environments,where advanced panoramic video processing and wireless communication technologies are essential to enable remote control and autonomous operation of unmanned ground vehicles(UGVs).However,conventional video surveillance systems suffer from several limitations,including limited field of view,high processing latency,low reliability,excessive resource consumption,and significant transmission delays.These shortcomings impede the widespread adoption of UGVs in battlefield settings.To overcome these challenges,this paper proposes a novel multi-channel video capture and stitching system designed for real-time video processing.The system integrates the Speeded-Up Robust Features(SURF)algorithm and the Fast Library for Approximate Nearest Neighbors(FLANN)algorithm to execute essential operations such as feature detection,descriptor computation,image matching,homography estimation,and seamless image fusion.The fused panoramic video is then encoded and assembled to produce a seamless output devoid of stitching artifacts and shadows.Furthermore,H.264 video compression is employed to reduce the data size of the video stream without sacrificing visual quality.Using the Real-Time Streaming Protocol(RTSP),the compressed stream is transmitted efficiently,supporting real-time remote monitoring and control of UGVs in dynamic battlefield environments.Experimental results indicate that the proposed system achieves high stability,flexibility,and low latency.With a wireless link latency of 30 ms,the end-to-end video transmission latency remains around 140 ms,enabling smooth video communication.The system can tolerate packet loss rates(PLR)of up to 20%while maintaining usable video quality(with latency around 200 ms).These properties make it well-suited for mobile communication scenarios demanding high real-time video performance.展开更多
As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,t...As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,the heterogeneity of aircraft,partial observability,and dynamic uncertainty in operational airspace pose significant challenges to autonomous collision avoidance using traditional methods.To address these issues,this paper proposes an adaptive collision avoidance approach for UAVs based on deep reinforcement learning.First,a unified uncertainty model incorporating dynamic wind fields is constructed to capture the complexity of joint operational environments.Then,to effectively handle the heterogeneity between manned and unmanned aircraft and the limitations of dynamic observations,a sector-based partial observation mechanism is designed.A Dynamic Threat Prioritization Assessment algorithm is also proposed to evaluate potential collision threats from multiple dimensions,including time to closest approach,minimum separation distance,and aircraft type.Furthermore,a Hierarchical Prioritized Experience Replay(HPER)mechanism is introduced,which classifies experience samples into high,medium,and low priority levels to preferentially sample critical experiences,thereby improving learning efficiency and accelerating policy convergence.Simulation results show that the proposed HPER-D3QN algorithm outperforms existing methods in terms of learning speed,environmental adaptability,and robustness,significantly enhancing collision avoidance performance and convergence rate.Finally,transfer experiments on a high-fidelity battlefield airspace simulation platform validate the proposed method's deployment potential and practical applicability in complex,real-world joint operational scenarios.展开更多
The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th...The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.展开更多
The transportation and logistics sectors are major contributors to Greenhouse Gase(GHG)emissions.Carbon dioxide(CO_(2))from Light-Duty Vehicles(LDVs)is posing serious risks to air quality and public health.Understandi...The transportation and logistics sectors are major contributors to Greenhouse Gase(GHG)emissions.Carbon dioxide(CO_(2))from Light-Duty Vehicles(LDVs)is posing serious risks to air quality and public health.Understanding the extent of LDVs’impact on climate change and human well-being is crucial for informed decisionmaking and effective mitigation strategies.This study investigates the predictability of CO_(2)emissions from LDVs using a comprehensive dataset that includes vehicles from various manufacturers,their CO_(2)emission levels,and key influencing factors.Specifically,sixMachine Learning(ML)algorithms,ranging fromsimple linearmodels to complex non-linear models,were applied under identical conditions to ensure a fair comparison and their performance metrics were calculated.The obtained results showed a significant influence of variables such as engine size on CO_(2)emissions.Although the six algorithms have provided accurate forecasts,the Linear Regression(LR)model was found to be sufficient,achieving a Mean Absolute Percentage Error(MAPE)below 0.90%and a Coefficient of Determination(R2)exceeding 99.7%.These findings may contribute to a deeper understanding of LDVs’role in CO_(2)emissions and offer actionable insights for reducing their environmental impact.In fact,vehicle manufacturers can leverage these insights to target key emission-related factors,while policymakers and stakeholders in logistics and transportation can use the models to estimate the CO_(2)emissions of new vehicles before their market deployment or to project future emissions from current and expected LDV fleets.展开更多
The proliferation of carrier aircraft and the integration of unmanned aerial vehicles(UAVs)on aircraft carriers present new challenges to the automation of launch and recovery operations.This paper investigates a coll...The proliferation of carrier aircraft and the integration of unmanned aerial vehicles(UAVs)on aircraft carriers present new challenges to the automation of launch and recovery operations.This paper investigates a collaborative scheduling problem inherent to the operational processes of carrier aircraft,where launch and recovery tasks are conducted concurrently on the flight deck.The objective is to minimize the cumulative weighted waiting time in the air for recovering aircraft and the cumulative weighted delay time for launching aircraft.To tackle this challenge,a multiple population self-adaptive differential evolution(MPSADE)algorithm is proposed.This method features a self-adaptive parameter updating mechanism that is contingent upon population diversity,an asynchronous updating scheme,an individual migration operator,and a global crossover mechanism.Additionally,comprehensive experiments are conducted to validate the effectiveness of the proposed model and algorithm.Ultimately,a comparative analysis with existing operation modes confirms the enhanced efficiency of the collaborative operation mode.展开更多
To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports,this paper proposes an enhanced MBS-YOLO(Multi-Branch Small Target...To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports,this paper proposes an enhanced MBS-YOLO(Multi-Branch Small Target Detection YOLO)model for anti-drone object detection,based on the YOLOv8 architecture.To overcome the limitations of existing methods in detecting small objects within complex backgrounds,we designed a C2f-Pu module with excellent feature extraction capability and a more compact parameter set,aiming to reduce the model’s computational complexity.To improve multi-scale feature fusion,we construct a Multi-Branch Feature Pyramid Network(MB-FPN)that employs a cross-level feature fusion strategy to enhance the model’s representation of small objects.Additionally,a shared detail-enhanced detection head is introduced to address the large size variations of Unmanned Aerial Vehicle(UAV)targets,thereby improving detection performance across different scales.Experimental results demonstrate that the proposed model achieves consistent improvements across multiple benchmarks.On the Det-Fly dataset,it improves precision by 3%,recall by 5.6%,and mAP50 by 4.5%compared with the baseline,while reducing parameters by 21.2%.Cross-validation on the VisDrone dataset further validates its robustness,yielding additional gains of 3.2%in precision,6.1%in recall,and 4.8%in mAP50 over the original YOLOv8.These findings confirm the effectiveness of the proposed algorithm in enhancing UAV detection performance under complex scenarios.展开更多
In response to the challenges faced by unmanned swarms in mountain obstacle-breaching missions within complex terrains,such as poor task-resource coupling,lengthy solution generation times,and poor inter-platform coll...In response to the challenges faced by unmanned swarms in mountain obstacle-breaching missions within complex terrains,such as poor task-resource coupling,lengthy solution generation times,and poor inter-platform collaboration,an unmanned swarm scheduling strategy tailored is proposed for mountain obstacle-breaching missions.Initially,by formalizing the descriptions of obstacle breaching operations,the swarm,and obstacle targets,an optimization model is constructed with the objectives of expected global benefit,timeliness,and task completion degree.A meta-task decomposition and reassembly strategy is then introduced to more precisely match the capabilities of unmanned platforms with task requirements.Additionally,a meta-task decomposition optimization model and a meta-task allocation operator are incorporated to achieve efficient allocation of swarm resources and collaborative scheduling.Simulation results demonstrate that the model can accurately generate reasonable and feasible obstacle breaching execution plans for unmanned swarms based on specific task requirements and environmental conditions.Moreover,compared to conventional strategies,the proposed strategy enhances task completion degree and expected returns while reducing the execution time of the plans.展开更多
In strategic decision-making tasks,determining how to assign limited costly resource towards the defender and the attacker is a central problem.However,it is hard for pre-allocated resource assignment to adapt to dyna...In strategic decision-making tasks,determining how to assign limited costly resource towards the defender and the attacker is a central problem.However,it is hard for pre-allocated resource assignment to adapt to dynamic fighting scenarios,and exists situations where the scenario and rule of the Colonel Blotto(CB)game are too restrictive in real world.To address these issues,a support stage is added as supplementary for pre-allocated results,in which a novel two-stage competitive resource assignment problem is formulated based on CB game and stochastic Lanchester equation(SLE).Further,the force attrition in these two stages is formulated as a stochastic progress to consider the complex fighting progress,including the case that the player with fewer resources defeats the player with more resources and wins the battlefield.For solving this two-stage resource assignment problem,nested solving and no-regret learning are proposed to search the optimal resource assignment strategies.Numerical experiments are taken to analyze the effectiveness of the proposed model and study the assignment strategies in various cases.展开更多
Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representati...Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representation,modeling,fusion,computation,and storage.Within this framework,knowledge extraction,as the core component,directly determines KG quality.In military domains,traditional manual curation models face efficiency constraints due to data fragmentation,complex knowledge architectures,and confidentiality protocols.Meanwhile,crowdsourced ontology construction approaches from general domains prove non-transferable,while human-crafted ontologies struggle with generalization deficiencies.To address these challenges,this study proposes an OntologyAware LLM Methodology for Military Domain Knowledge Extraction(LLM-KE).This approach leverages the deep semantic comprehension capabilities of Large Language Models(LLMs)to simulate human experts’cognitive processes in crowdsourced ontology construction,enabling automated extraction of military textual knowledge.It concurrently enhances knowledge processing efficiency and improves KG completeness.Empirical analysis demonstrates that this method effectively resolves scalability and dynamic adaptation challenges in military KG construction,establishing a novel technological pathway for advancing military intelligence development.展开更多
Outer space is humanity’s most vital future frontier;it possesses unique geopolitical and strategic attributes with significant implications for national development and security.Over recent decades,the strategic val...Outer space is humanity’s most vital future frontier;it possesses unique geopolitical and strategic attributes with significant implications for national development and security.Over recent decades,the strategic value of outer space—spanning political,economic,military,technological,and societal domains—has steadily grown,driving new competition among major powers for access to space resources and related rights.Within this rivalry,military capabilities in space have emerged as the decisive foundation of space competition.As a result,major powers have increasingly directed investments toward space-based defense programs and security infrastructures,recognizing that military superiority in space is the new strategic high ground in their rivalry.展开更多
An increase in the use of the gun barrel will cause wear of the inner wall,which reduces the muzzle velocity and the spin rate of the projectile.The off-bore flight attitude and trajectory of the projectile also chang...An increase in the use of the gun barrel will cause wear of the inner wall,which reduces the muzzle velocity and the spin rate of the projectile.The off-bore flight attitude and trajectory of the projectile also change,affecting the shooting power and the accuracy.Exterior ballistic data of a high-speed spinning projectile are required to study the performance change.Therefore,based on the barrel’s accelerated life test,the whole process of projectile shooting is reproduced using numerical simulation technology,and key information on the ballistic performance change at each shooting stage are acquired.Studies have shown that in the later stages of barrel shooting,the accuracy of shooting has not decreased significantly.However,it is found that the angle of attack of the projectile increases as the wear of the barrel increases.The maximum angle of attack reaches 0.106 rad when the number of shots reaches 4300.Meanwhile,elliptical bullet hole has appeared on the target at this shooting stage.Through combining external ballistic theory with simulation results,the primary reason of this phenomenon is found to be a significant decrease in the muzzle spin rate of the projectile.At the end of the barrel life,the projectile muzzle spin rate is 57.5%lower than that of a barrel without wear.展开更多
War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient an...War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.展开更多
Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially in...Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.展开更多
Battlefield situation awareness is equivalent to the observation and orientation part of the Observation-Orientation-Decision-Action(OODA)loop.It is the key to transforming information advantage into decision-making a...Battlefield situation awareness is equivalent to the observation and orientation part of the Observation-Orientation-Decision-Action(OODA)loop.It is the key to transforming information advantage into decision-making advantages and an essential support for agile command decision-making.With the rapid rise and widespread application of various advanced intelligent technologies and model algorithms in the military field,traditional situation awareness technology and capacity building are facing great challenges.Relying solely on manual processing of battlefield situational data is no longer sufficient to meet the changing needs of the battlefield,nor can it provide commanders with real-time,accurate,efficient,and reliable situational information.Therefore,it is necessary to use highperformance hardware facilities and artificial intelligence technology to process massive battlefield data and provide an important reference basis for commanders to implement decision-making behaviors.展开更多
Electronic warfare is a modern combat mode,in which predicting digital material consumption is a key for material requirements planning(MRP).In this paper,we introduce an insensitive loss function(ε) and propose a ε...Electronic warfare is a modern combat mode,in which predicting digital material consumption is a key for material requirements planning(MRP).In this paper,we introduce an insensitive loss function(ε) and propose a ε-SVR-based prediction approach.First,we quantify values of influencing factors of digital equipments in electronic warfare and a small-sample data on real consumption to form a real combat data set,and preprocess it to construct the sample space.Subsequently,we establish the ε-SVR-based prediction model based on "wartime influencing factors-material consumption" and perform model training.In case study,we give 8 historical battle events with battle damage data and predict 3 representative kinds of digital materials by using the proposed approach.The results illustrate its higher accuracy and more convenience compared with other current approaches.Taking data acquisition controller prediction as an example,our model has better prediction performance(RMSE=0.575 7,MAPE(%)=12.037 6 and R^2=0.996 0) compared with BP neural network model(RMSE=1.272 9,MAPE(%)=23.577 5 and R^2=0.980 3) and GM(1,1) model(RMSE=2.095 0,MAPE(%)=24.188 0 and R^2=0.946 6).The fact shows that the approach can be used to support decision-making for MRP in electronic warfare.展开更多
文摘Defence Technology(ISSN 2214-9147(O);2096-3459(P)),sponsored by China Ordnance Society,is published monthly and aims to become one of the well-known comprehensive journals in the world,which reports on the breakthroughs in defence technology by building up an international academic exchange platform for the defence technology related research.It publishes original research papers having direct bearing on defence,with a balanced coverage on analytical,experimental,numerical simulation and applied investigations.It covers various disciplines of science,technology and engineering.
文摘Military image encryption plays a vital role in ensuring the secure transmission of sensitive visual information from unauthorized access.This paper proposes a new Tri-independent keying method for encrypting military images.The proposed encryption method is based on multilevel security stages of pixel-level scrambling,bitlevel manipulation,and block-level shuffling operations.For having a vast key space,the input password is hashed by the Secure Hash Algorithm 256-bit(SHA-256)for generating independently deterministic keys used in the multilevel stages.A piecewise pixel-level scrambling function is introduced to perform a dual flipping process controlled with an adaptive key for obscuring the spatial relationships between the adjacent pixels.Adynamicmasking scheme is presented for conducting a bit-level manipulation based on distinct keys that change over image regions,providing completely different encryption results on identical regions.To handle the global correlation between large-scale patterns,a chaotic index-map system is employed for shuffling image regions randomly across the image domain based on a logistic map seeded with a private key.Experimental results on a dataset of military images show the effectiveness of the proposed encryption method in producing excellent quantitative and qualitative results.The proposed method obtains uniform histogram distributions,high entropy values around the ideal(≈8 bits),Number of Pixel Change Rate(NPCR)values above 99.5%,and low Peak Signal-to-Noise Ratio(PSNR)over all encrypted images.This validates the robustness of the proposed method against cryptanalytic attacks,verifying its ability to serve as a practical basis for secure image transmission in defense systems.
基金supported by the Natural Science Foundation of Shaanxi Province(2023-JC-QN-0728)the China Postdoctoral Science Foundation(2021M693942)。
文摘Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation plan is a key step in the organizational planning.Based on the description of the problem and the definition of organizational elements,the matching model of platform-target attack wave is constructed to minimize the redundancy of command and decision-making capability,resource capability and the number of platforms used.Based on the non-dominated sorting genetic algorithmⅢ(NSGA-Ⅲ)framework,which includes encoding/decoding method and constraint handling method,the generation model of organizational force formation plan is solved,and the effectiveness and superiority of the algorithm are verified by simulation experiments.
文摘In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.
基金supported by the National Natural Science Foundation of China(Grant No.72334003)the National Key Research and Development Program of China(Grant No.2022YFB2702804)+1 种基金the Shandong Key Research and Development Program(Grant No.2020ZLYS09)the Jinan Program(Grant No.2021GXRC084-2).
文摘With the continuous advancement of unmanned technology in various application domains,the development and deployment of blind-spot-free panoramic video systems have gained increasing importance.Such systems are particularly critical in battlefield environments,where advanced panoramic video processing and wireless communication technologies are essential to enable remote control and autonomous operation of unmanned ground vehicles(UGVs).However,conventional video surveillance systems suffer from several limitations,including limited field of view,high processing latency,low reliability,excessive resource consumption,and significant transmission delays.These shortcomings impede the widespread adoption of UGVs in battlefield settings.To overcome these challenges,this paper proposes a novel multi-channel video capture and stitching system designed for real-time video processing.The system integrates the Speeded-Up Robust Features(SURF)algorithm and the Fast Library for Approximate Nearest Neighbors(FLANN)algorithm to execute essential operations such as feature detection,descriptor computation,image matching,homography estimation,and seamless image fusion.The fused panoramic video is then encoded and assembled to produce a seamless output devoid of stitching artifacts and shadows.Furthermore,H.264 video compression is employed to reduce the data size of the video stream without sacrificing visual quality.Using the Real-Time Streaming Protocol(RTSP),the compressed stream is transmitted efficiently,supporting real-time remote monitoring and control of UGVs in dynamic battlefield environments.Experimental results indicate that the proposed system achieves high stability,flexibility,and low latency.With a wireless link latency of 30 ms,the end-to-end video transmission latency remains around 140 ms,enabling smooth video communication.The system can tolerate packet loss rates(PLR)of up to 20%while maintaining usable video quality(with latency around 200 ms).These properties make it well-suited for mobile communication scenarios demanding high real-time video performance.
基金supported by the National Key Research and Development Program of China(No.2022YFB4300902).
文摘As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,the heterogeneity of aircraft,partial observability,and dynamic uncertainty in operational airspace pose significant challenges to autonomous collision avoidance using traditional methods.To address these issues,this paper proposes an adaptive collision avoidance approach for UAVs based on deep reinforcement learning.First,a unified uncertainty model incorporating dynamic wind fields is constructed to capture the complexity of joint operational environments.Then,to effectively handle the heterogeneity between manned and unmanned aircraft and the limitations of dynamic observations,a sector-based partial observation mechanism is designed.A Dynamic Threat Prioritization Assessment algorithm is also proposed to evaluate potential collision threats from multiple dimensions,including time to closest approach,minimum separation distance,and aircraft type.Furthermore,a Hierarchical Prioritized Experience Replay(HPER)mechanism is introduced,which classifies experience samples into high,medium,and low priority levels to preferentially sample critical experiences,thereby improving learning efficiency and accelerating policy convergence.Simulation results show that the proposed HPER-D3QN algorithm outperforms existing methods in terms of learning speed,environmental adaptability,and robustness,significantly enhancing collision avoidance performance and convergence rate.Finally,transfer experiments on a high-fidelity battlefield airspace simulation platform validate the proposed method's deployment potential and practical applicability in complex,real-world joint operational scenarios.
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia,project number MoE-IF-UJ-R2-22-20772-1.
文摘The transportation and logistics sectors are major contributors to Greenhouse Gase(GHG)emissions.Carbon dioxide(CO_(2))from Light-Duty Vehicles(LDVs)is posing serious risks to air quality and public health.Understanding the extent of LDVs’impact on climate change and human well-being is crucial for informed decisionmaking and effective mitigation strategies.This study investigates the predictability of CO_(2)emissions from LDVs using a comprehensive dataset that includes vehicles from various manufacturers,their CO_(2)emission levels,and key influencing factors.Specifically,sixMachine Learning(ML)algorithms,ranging fromsimple linearmodels to complex non-linear models,were applied under identical conditions to ensure a fair comparison and their performance metrics were calculated.The obtained results showed a significant influence of variables such as engine size on CO_(2)emissions.Although the six algorithms have provided accurate forecasts,the Linear Regression(LR)model was found to be sufficient,achieving a Mean Absolute Percentage Error(MAPE)below 0.90%and a Coefficient of Determination(R2)exceeding 99.7%.These findings may contribute to a deeper understanding of LDVs’role in CO_(2)emissions and offer actionable insights for reducing their environmental impact.In fact,vehicle manufacturers can leverage these insights to target key emission-related factors,while policymakers and stakeholders in logistics and transportation can use the models to estimate the CO_(2)emissions of new vehicles before their market deployment or to project future emissions from current and expected LDV fleets.
文摘The proliferation of carrier aircraft and the integration of unmanned aerial vehicles(UAVs)on aircraft carriers present new challenges to the automation of launch and recovery operations.This paper investigates a collaborative scheduling problem inherent to the operational processes of carrier aircraft,where launch and recovery tasks are conducted concurrently on the flight deck.The objective is to minimize the cumulative weighted waiting time in the air for recovering aircraft and the cumulative weighted delay time for launching aircraft.To tackle this challenge,a multiple population self-adaptive differential evolution(MPSADE)algorithm is proposed.This method features a self-adaptive parameter updating mechanism that is contingent upon population diversity,an asynchronous updating scheme,an individual migration operator,and a global crossover mechanism.Additionally,comprehensive experiments are conducted to validate the effectiveness of the proposed model and algorithm.Ultimately,a comparative analysis with existing operation modes confirms the enhanced efficiency of the collaborative operation mode.
基金supported by the Key R&D Programof Xianyang City,Shaanxi Province(L2024-ZDYF-ZDYF-GY-0043).
文摘To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports,this paper proposes an enhanced MBS-YOLO(Multi-Branch Small Target Detection YOLO)model for anti-drone object detection,based on the YOLOv8 architecture.To overcome the limitations of existing methods in detecting small objects within complex backgrounds,we designed a C2f-Pu module with excellent feature extraction capability and a more compact parameter set,aiming to reduce the model’s computational complexity.To improve multi-scale feature fusion,we construct a Multi-Branch Feature Pyramid Network(MB-FPN)that employs a cross-level feature fusion strategy to enhance the model’s representation of small objects.Additionally,a shared detail-enhanced detection head is introduced to address the large size variations of Unmanned Aerial Vehicle(UAV)targets,thereby improving detection performance across different scales.Experimental results demonstrate that the proposed model achieves consistent improvements across multiple benchmarks.On the Det-Fly dataset,it improves precision by 3%,recall by 5.6%,and mAP50 by 4.5%compared with the baseline,while reducing parameters by 21.2%.Cross-validation on the VisDrone dataset further validates its robustness,yielding additional gains of 3.2%in precision,6.1%in recall,and 4.8%in mAP50 over the original YOLOv8.These findings confirm the effectiveness of the proposed algorithm in enhancing UAV detection performance under complex scenarios.
基金supported by the National Natural Science Foundation of China(61374186)。
文摘In response to the challenges faced by unmanned swarms in mountain obstacle-breaching missions within complex terrains,such as poor task-resource coupling,lengthy solution generation times,and poor inter-platform collaboration,an unmanned swarm scheduling strategy tailored is proposed for mountain obstacle-breaching missions.Initially,by formalizing the descriptions of obstacle breaching operations,the swarm,and obstacle targets,an optimization model is constructed with the objectives of expected global benefit,timeliness,and task completion degree.A meta-task decomposition and reassembly strategy is then introduced to more precisely match the capabilities of unmanned platforms with task requirements.Additionally,a meta-task decomposition optimization model and a meta-task allocation operator are incorporated to achieve efficient allocation of swarm resources and collaborative scheduling.Simulation results demonstrate that the model can accurately generate reasonable and feasible obstacle breaching execution plans for unmanned swarms based on specific task requirements and environmental conditions.Moreover,compared to conventional strategies,the proposed strategy enhances task completion degree and expected returns while reducing the execution time of the plans.
基金supported by the National Natural Science Foundation of China(61702528,61806212,62173336)。
文摘In strategic decision-making tasks,determining how to assign limited costly resource towards the defender and the attacker is a central problem.However,it is hard for pre-allocated resource assignment to adapt to dynamic fighting scenarios,and exists situations where the scenario and rule of the Colonel Blotto(CB)game are too restrictive in real world.To address these issues,a support stage is added as supplementary for pre-allocated results,in which a novel two-stage competitive resource assignment problem is formulated based on CB game and stochastic Lanchester equation(SLE).Further,the force attrition in these two stages is formulated as a stochastic progress to consider the complex fighting progress,including the case that the player with fewer resources defeats the player with more resources and wins the battlefield.For solving this two-stage resource assignment problem,nested solving and no-regret learning are proposed to search the optimal resource assignment strategies.Numerical experiments are taken to analyze the effectiveness of the proposed model and study the assignment strategies in various cases.
文摘Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representation,modeling,fusion,computation,and storage.Within this framework,knowledge extraction,as the core component,directly determines KG quality.In military domains,traditional manual curation models face efficiency constraints due to data fragmentation,complex knowledge architectures,and confidentiality protocols.Meanwhile,crowdsourced ontology construction approaches from general domains prove non-transferable,while human-crafted ontologies struggle with generalization deficiencies.To address these challenges,this study proposes an OntologyAware LLM Methodology for Military Domain Knowledge Extraction(LLM-KE).This approach leverages the deep semantic comprehension capabilities of Large Language Models(LLMs)to simulate human experts’cognitive processes in crowdsourced ontology construction,enabling automated extraction of military textual knowledge.It concurrently enhances knowledge processing efficiency and improves KG completeness.Empirical analysis demonstrates that this method effectively resolves scalability and dynamic adaptation challenges in military KG construction,establishing a novel technological pathway for advancing military intelligence development.
基金supported by the National Nature Science Foundation of China(61304223)the Aeronautical Science Foundation of China(2016ZA52009)the Research Fund for the Doctoral Program of Higher Education of China(20123218120015)
文摘Outer space is humanity’s most vital future frontier;it possesses unique geopolitical and strategic attributes with significant implications for national development and security.Over recent decades,the strategic value of outer space—spanning political,economic,military,technological,and societal domains—has steadily grown,driving new competition among major powers for access to space resources and related rights.Within this rivalry,military capabilities in space have emerged as the decisive foundation of space competition.As a result,major powers have increasingly directed investments toward space-based defense programs and security infrastructures,recognizing that military superiority in space is the new strategic high ground in their rivalry.
基金financially supported by Beijing Key Laboratory for Corrosion-Erosion and Surface Technology,Beijing Municipal Education Commission Project(SYS100080419)。
文摘An increase in the use of the gun barrel will cause wear of the inner wall,which reduces the muzzle velocity and the spin rate of the projectile.The off-bore flight attitude and trajectory of the projectile also change,affecting the shooting power and the accuracy.Exterior ballistic data of a high-speed spinning projectile are required to study the performance change.Therefore,based on the barrel’s accelerated life test,the whole process of projectile shooting is reproduced using numerical simulation technology,and key information on the ballistic performance change at each shooting stage are acquired.Studies have shown that in the later stages of barrel shooting,the accuracy of shooting has not decreased significantly.However,it is found that the angle of attack of the projectile increases as the wear of the barrel increases.The maximum angle of attack reaches 0.106 rad when the number of shots reaches 4300.Meanwhile,elliptical bullet hole has appeared on the target at this shooting stage.Through combining external ballistic theory with simulation results,the primary reason of this phenomenon is found to be a significant decrease in the muzzle spin rate of the projectile.At the end of the barrel life,the projectile muzzle spin rate is 57.5%lower than that of a barrel without wear.
文摘War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.
基金supported by the National Natural Science Foundation of China(Nos.61771027,61071139,61471019,61671035)supported in part under the Royal Society of Edinburgh-National Natural Science Foundation of China(RSE-NNSFC)Joint Project(2017–2019)(No.6161101383)with China University of Petroleum(Huadong)partially supported by the UK Engineering and Physical Sciences Research Council(EPSRC)(Nos.EP/I009310/1,EP/M026981/1)
文摘Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.
文摘Battlefield situation awareness is equivalent to the observation and orientation part of the Observation-Orientation-Decision-Action(OODA)loop.It is the key to transforming information advantage into decision-making advantages and an essential support for agile command decision-making.With the rapid rise and widespread application of various advanced intelligent technologies and model algorithms in the military field,traditional situation awareness technology and capacity building are facing great challenges.Relying solely on manual processing of battlefield situational data is no longer sufficient to meet the changing needs of the battlefield,nor can it provide commanders with real-time,accurate,efficient,and reliable situational information.Therefore,it is necessary to use highperformance hardware facilities and artificial intelligence technology to process massive battlefield data and provide an important reference basis for commanders to implement decision-making behaviors.
基金funded by National Natural Science Foundation of China(grant number 61473311,70901075)Natural Science Foundation of Beijing Municipality(grant number 9142017)military projects funded by the Chinese Army。
文摘Electronic warfare is a modern combat mode,in which predicting digital material consumption is a key for material requirements planning(MRP).In this paper,we introduce an insensitive loss function(ε) and propose a ε-SVR-based prediction approach.First,we quantify values of influencing factors of digital equipments in electronic warfare and a small-sample data on real consumption to form a real combat data set,and preprocess it to construct the sample space.Subsequently,we establish the ε-SVR-based prediction model based on "wartime influencing factors-material consumption" and perform model training.In case study,we give 8 historical battle events with battle damage data and predict 3 representative kinds of digital materials by using the proposed approach.The results illustrate its higher accuracy and more convenience compared with other current approaches.Taking data acquisition controller prediction as an example,our model has better prediction performance(RMSE=0.575 7,MAPE(%)=12.037 6 and R^2=0.996 0) compared with BP neural network model(RMSE=1.272 9,MAPE(%)=23.577 5 and R^2=0.980 3) and GM(1,1) model(RMSE=2.095 0,MAPE(%)=24.188 0 and R^2=0.946 6).The fact shows that the approach can be used to support decision-making for MRP in electronic warfare.
