bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Motion Coupling Interior Ballistics of Cased Telescoped Ammunition Artillery
Authors: Shi Junfei , Xu Kai , Xie Yao , Liu Aifeng 
Published: December 2024

Abstract
In this paper, the motion-coupled interior Ballistics of cased telescoped ammunition artillery is studied, and the physical process of the real interior ballistic process is analysed, on the basis of which, the gunpowder-gas energy conversion law at different stages and the combustion characteristics of the combustible guide cylinder are analysed, and the charge form function of the combustible guide cylinder material is established. Based on the classical interior ballistics theory, the motion-coupled interior ballistic theoretical model of cased telescoped ammunition artillery is established, taking into account the matching characteristics of the actual projectile and artillery movement. Combined with the energy conversion characteristics of the three phases, the set of internal ballistic control equations of cased telescoped ammunition artillery is numerically solved by the thermodynamic coupling and the fourth-order Lungkuta method, and the correctness of the theoretical model of the interior ballistics theory is verified by the firing test, and the results of the study can provide theoretical guidance for the related research and engineering application of the launching and controlling of cased telescoped ammunition artillery.
Research on flight intention recognition of UAVs-swarm
Authors: Zhang Genyuan , Qi Wanlong , Yu Xueyuan , Zhang Zhipeng , Lin Zhiwei 
Published: December 2024

Abstract
Most existing threat level assessments for aerial targets rely on numerical analysis and typically address only single or a few incoming targets without incorporating intention recognition. Such methods are inadequate for anti-UAV(Unmanned aerial vehicle) tasks involving large numbers of flight targets. To accurately and efficiently identify the flight intentions of UAVs-swarm, this paper proposes a method based on formation recognition and trajectory prediction. Initially, the trajectories of UAVs are obtained and clustered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. For each cluster, a Bayesian classifier is employed to recognize its flight formation. Subsequently, a minimum enclosing sphere is established for each cluster, and trajectory fusion and prediction are conducted based on the sphere’s center. Finally, a flight intention recognition model is constructed. The results demonstrate that this method effectively infers the flight intentions of each UAVs-cluster, providing a robust basis for the threat level assessment in anti-UAVs-swarm tasks.
Embedded deep learning video stabilization based on multiprocessing
Authors: Wang Fangyi , Zhong Bihua , Liu Tao , Lin Jiajun , Bai Hongyang , Wan Gang 
Published: December 2024

Abstract
With the gradual development of modern war in the direction of informationization and intelligentization, image guidance has established a prominent position in precision guidance technology with its high guidance accuracy, strong anti-jamming ability and precise hit probability, so the image seeker has become the focus of competitive development. However, missile jitter during flight, influenced by external disturbances and projectile rigidity, seriously affects the video quality captured by the camera in the image seeker, thereby reducing the detection accuracy of target recognition and tracking. In addition, the computational resources of the missile-mounted embedded platform are limited, and algorithms based on deep learning compute slowly. To address these issues, we have proposed a lightweight video stabilization framework and harnessed the potential of multi-core embedded devices through multiprocessing to achieve higher operating speeds. We tested our algorithm on an NVIDIA Jetson Orin Nano device and achieved a speed improvement of nearly 30% compared to an algorithm for serial computation.
Adaptive robust control of tank gun pitching stabilization system considering uncertainty
Authors: Zhou Ruohan , Yao Jianyong , Deng Wenxiang , Yuan Shusen 
Published: December 2024

Abstract
Aiming at the complex nonlinearity, uncertainty and coupling of the tank gun pitching stabilization system, an adaptive robust control strategy for the tank gun pitching stabilization system is proposed by combining adaptive control and deterministic robust control. Considering that the tank gun pitching stabilization system is a kind of uncertainty as well as nonlinear coupled dynamics system, a mechanical dynamics model and an electrical control model under the high mobility environment are established. Based on the backstepping concept fusion adaptive robust idea, the parameter adaptive law is constructed to eliminate the effect of unknown system parameter, and the adaptive robust function is designed to inhibit the interference for unmodeled disturbance of the system control precision. The designed controller synthesizes the advantages of adaptive control methods as well as robust control methods to improve the practical value in engineering. Based on Lyapunov theory, the analysis indicates that the tank gun pitching stabilization system can be controlled asymptotically only by unknown parameter effects, and the ideal stabilization accuracy of the system can be ensured when both parameter uncertainty and uncertain nonlinearity exist in the system. A large number of comparative co-simulations based on Recurdyn-Simulink demonstrate the superiority of the presented technique in this study.
A-star Ant Colony Fusion Optimisation for Unmanned Ground Vehicle Route Planning
Authors: Hou Rong , Zhang Pengfei 
Published: December 2024

Abstract
Route planning technology of Unmanned Ground Vehicle (UGV) is a key issue in robotics, with the goal of avoiding obstacles and moving quickly to the target point. Aiming at the problem of easy collision with obstacles when utilizing the conventional A-star algorithm for global route planning, an improved turning mechanism is proposed to avoid collision. Aiming at the problems of longer routes and insufficiently smooth routes introduced by it, an improved A-star ant colony fusion algorithm for UGV route planning is proposed by combining the local planning capability of the ant colony algorithm. The A-star algorithm with an improved turning mechanism is fused with an ant colony algorithm to iteratively optimize the route length and route smoothness progressively. Simulation results show that the improved A-star ant colony fusion algorithm is able to avoid obstacles, reduce the length of the planned route by at least 2.34%, and improve the route smoothness by at least 5.62% compared to the A-star algorithm. It is beneficial for UGV to accomplish the corresponding tasks quickly on the basis of ensuring its obstacle avoidance.
Research on the game confrontation technology of UAV cluster
Authors: Zhuo Cao , Jian Shen , Qichen Yan , Ziwang Zhang , Fan Liu , Binna He 
Published: December 2024

Abstract
A reinforcement learning model is proposed for the problem of UAV cluster autonomy in air combat environment. A theoretical motion model was established for the quadcopter unmanned aerial vehicle to describe its motion states, and a framework for adversarial algorithms based on reinforcement learning was proposed. A series of simulation experiments are carried out to verify the influence of quadcopter UAV parameters change on the cluster game performance of both quadcopter UAV by constantly adjusting the parameter setting of quadcopter UAV.
Research on Adaptive Kalman Filtering Adjustment Based on Support Vector Regression and Bayesian Optimization
Authors: Zhang Chen , Guan Xueyuan , Cai Xiu , Wang Yanyi 
Published: December 2024

Abstract
This study addresses the performance optimization of Kalman filters in combined navigation systems, particularly the dynamic adjustment of the measurement noise covariance matrix R. An innovative adaptive method that incorporates Support Vector Regression (SVR) and Bayesian optimization techniques is proposed to achieve real-time optimization of the R value. This study initially utilizes the Support Vector Regression model, with innovation data as input, to predict the most suitable R values. Subsequently, the Bayesian optimization algorithm is applied to optimize the key parameters of the SVR model, thus enhancing the accuracy and response speed of the predictions. Comparative experiments with traditional methods demonstrate that the proposed adaptive adjustment method significantly improves the filtering accuracy and stability of the combined navigation system, especially under dynamically changing environmental conditions. This research not only provides a new approach to parameter optimization for Kalman filters but also offers an effective solution for similar real-time system dynamic adjustment issues.
A Research on Recommendation Method of Kill Chain Based on Graph Theory Algorithm
Authors: Wang Di , Wu Kun , Li Jun , Guo Zhiming , Sun Yong , Bai Zilong , Song Yixuan 
Published: December 2024

Abstract
The present study introduces an optimization method based on graph theory to address the kill chain recommendation problem in rapidly changing modern battlefield environments. This method transforms the kill chain selection problem into a shortest path problem with time constraints and designs an efficient optimization process using branch-and-bound algorithm. This study constructs an evaluation model for kill chain efficacy and time utility to ensure that the recommended kill chain achieves optimal efficacy within the target’s active strike time. The efficacy and efficiency of the proposed algorithm in handling multi-target scenarios have been substantiated through the analysis of data procured from the “2020 Joint Operations Smart Game Challenge”, reducing the optimization time when compared to traditional methods. This study provides a new perspective and technical assistance to enhance the intelligence and automation of operational command decision-making processes.
Path optimization of unmanned vehicle based on ant colony algorithm
Authors: Qi Li , Haotian Zheng , Jian Wang , Xincheng Zhang , Zezhong Chong 
Published: December 2024

Abstract
In this paper, we use the ant colony algorithm(ACO) and the river formation dynamics(RFD) algorithm to solve the path optimisation problem of unmanned vehicle in grid models. The traditional ACO algorithm only considers the relationship between the current point and the next point in the probability selection, but the optimised ACO algorithm takes the relative relationship between the next point and the target point into account, which effectively avoids local stagnation and improves the search speed. Aiming at the problems of mutual interference of paths and slow reinforcement of new paths that may occur in the routing process of ACO algorithm, we introduced the river formation dynamics algorithm. The RFD algorithm achieves gradient-directed optimisation of ACO algorithm by adding height attributes to the nodes in the graph and deciding the selection probability according to the gradient between two points. Experimental results show that the hybrid algorithm combining the ACO and RFD algorithms exhibits stronger optimisation finding ability in path optimisation problems. We applied the ACO, RFD algorithm, and the hybrid algorithm to these complex path optimisation problems and achieved significant results.
Aerodynamic Angle Estimation in Rotating Projectiles: A NBCA Fusion Approach
Authors: Huai-Wei A , Fu Jian , Wang Liang-Ming 
Published: December 2024

Abstract
The angle of attack (AOA) is a crucial parameter for describing the flight state of a projectile and is one of the key elements in external ballistic testing. In light of the inherent difficulty and substantial cost associated with the direct detection of the AOA during the flight of guided projectiles, this study introduces an estimation method for the AOA that is predicated on a neuro-behavioural cognitive architecture (NBCA) neural network fusion model. This approach leverages data from geomagnetic sensors to calculate the geomagnetic azimuth angle related to the AOA. Subsequently, by fitting the geomagnetic azimuth angle and angle of attack-related quantities into the fusion model and training the neural network, the angle of attack is estimated. Research results indicate that the root mean square error of the angle of attack estimation is 0.0149°. This method achieves optimal estimation of the projectile’s angle of attack without the need for extensive detection equipment, providing a novel perspective for the practical engineering application of projectile angle of attack detection.
Design of a Software-Defined Artillery System Architecture
Authors: Hao Bai , Tegang Lu , Zhengzheng Yang , Sheng Zhao , Wufan Sun 
Published: December 2024

Abstract
The rapid development of software-defined technology aligns closely with future trends in artillery system architectures. This paper analyzes these trends and the technical requirements for future artillery development, proposing a software-defined artillery system architecture to meet the need for flexibility and rapid advancement. The proposed architecture features a layered design, standardized interfaces, generalized hardware, software-defined functions, and resource virtualization. By employing a service-oriented software architecture, this system facilitates dynamic adjustment and expansion of artillery functions, thereby shortening the equipment development cycle and improving system flexibility and scalability. Consequently, this architecture can significantly enhance the operational effectiveness and adaptability of artillery systems in complex battlefield environments.
Research on the Power Line Carrier Communication Network System for Vehicle-mounted Weapons
Authors: Chi Zhang , Shaohui Xue , Yongliang Yang , Xiangang Sun , Fengge Wang 
Published: December 2024

Abstract
Power line carrier communication is a unique communication method in the power system, which utilizes existing power lines to transmit analog or digital signals at high speed through carrier communication. Power line communication is widely used due to its advantages of low investment, no need for rewiring, wide application range, and low maintenance cost. This article combines the extensive user needs of power line communication in various application fields, and designs a power line carrier communication network system with wide universality, strong scalability, fast communication speed, and high security. It has a strong supporting role in promoting the intelligent information perception of vehicle mounted weapons, advanced firepower control of manned/unmanned equipment, intelligent human-machine interaction, multi platform collaboration, and group intelligent collaborative air defense combat.
CBISP-AIS: Component-Based Intelligent Software Platform for Artillery Integrated Systems
Authors: Qi Zhiqiang , Zhang Cheng , Wang Binyi , Liao Rui , Yao Tong 
Published: December 2024

Abstract
Due to the variety of hardware types in integrated artillery systems, the information processing software faces challenges such as redundant low-level development, poor inheritance between systems, and difficulties in reusing software modules during iterative hardware and software upgrades. This paper presents the construction of a component-based intelligent software platform for integrated artillery systems (CBISP-AIS). CBISP-AIS modularizes and generalizes the integrated artillery information system, breaking down its development into corresponding functional components. CBISP-AIS enables an integrated design process encompassing simulation, design, coding, and verification of system application functions, facilitating faster function implementation and module deployment. Finally, CBISP-AIS is applied to weapon system development, with its effectiveness and reusability validated through case studies.
Gun Position Servo System Auto Tuning Control Based on Correlation Identification Method
Authors: Li Changhong , Han Chongwei , Zhu Chao 
Published: December 2024

Abstract
Position servo systems are used in kinds of gun weapons widely. It plays an important role for aiming to ground target or tracking to moving target. In this paper, a gun position servo system auto tuning control based on correlation identification method is presented. The M-sequence generator is constructed which is as a stimulus signal of the plant. Through signals acquisition of input and output of the plant speed loop, the cross-correlation function of the speed loop input and output can be calculated. Then the impulse response function of the position servo system plant speed loop can be identified. According to the identification results, position controller parameters are tuned based on the given width mid-frequency interval of the open loop position servo system. Last, simulations are carried out to a gun position servo system. The simulation results show that the identified impulse response function has high accuracy. Through the parameters tuning, the stable margin can be maintained which is determined by the width of mid-frequency interval for different speed loop time constant.
LSTM-GA-SVR based launch time window prediction algorithm
Authors: Zhang Quan , Zhao Zi-jie , Xu Li-wen 
Published: December 2024

Abstract
The strike-type unmanned underwater vehicle (UUV) is susceptible to external factors such as ocean currents during attack missions, resulting in unavoidable non-linear attitude fluctuations that have an adverse impact on the weapon hit rate of the UUV. To meet the requisite accuracy for target hits of strike-type UUVs in the presence of attitude fluctuations, this paper proposes an LSTM-GA-SVR launch time window prediction algorithm for short-term attitude prediction of UUVs. This algorithm combines a Long Short-Term Memory (LSTM) neural network, a Genetic Algorithm (GA) and Support Vector Regression (SVR) to address the non-linear motion characteristics of the UUV and enhance the generalization ability of the prediction. The Genetic Algorithm (GA) is employed to optimize the Support Vector Regression (SVR) model parameters, thereby enhancing the accuracy of the fitted data. The LSTM prediction model is introduced to capture long-term dependencies in the data and extract complex features, thus further optimizing short-term attitude prediction. The loss function is optimized using the least squares method, thereby achieving optimal weighting of the model, and improving prediction accuracy. The efficacy of the algorithm is then corroborated through simulation and experimentation. The results demonstrate that, under specific circumstances, the algorithm can achieve the predetermined target of deep displacement error <0.1m and yaw angle error <0.05rad. The model prediction time window reaches 88% of the actual window, thereby providing a relatively accurate launch time window based on measured data.
Path Planning for Unmanned Systems Based on Integrated Sampling Strategies and Improved PSO
Authors: Gao Wenjie , Wang Qiang , Hu Shengrong 
Published: December 2024

Abstract
B-splines and Particle Swarm Optimization algorithms are integrated for unmanned system path planning in mountainous terrains. In the early stages of the optimization search, the traditional Particle Swarm Optimization (PSO) algorithm achieves rapid convergence. However, as the process continues, it often struggles with local optima in later stages. To address this limitation, this research proposes an improved PSO algorithm that combines the Immune Algorithm (IMA) and Latin Hypercube Sampling Method. This enhancement bolsters the optimization capabilities of particles at different phases of the search by implementing an evaluation mechanism and dynamic weight adjustments. Experimental results demonstrate that, when confronting optimization challenges within complex mountainous terrains, the improved PSO algorithm (SIPSO) which is combined with IMA and Sampling Method significantly outperforms conventional PSO and Genetic Algorithm (GA) in both iteration counts and computational efficiency, showcasing a notable advancement in performance.
Research on safety technology of intelligent aviation ammunition system
Authors: Yongzhi Shan , Xianglei Meng , Long Zhang , Bangqi He , Liangchen Xu , Li-Feng Hu 
Published: December 2024

Abstract
With the continuous development of military technology, intelligent aviation ammunition has become an important part of modern military operations. However, the safety of intelligent aviation munitions systems has always been a matter of concern. This study aims to explore the safety technology of intelligent aviation ammunition systems to ensure their reliability and stability in combat. First, this study reviews the current development status and application fields of intelligent aviation ammunition systems. Subsequently, it focused on analyzing the security threats and risks that intelligent aviation ammunition systems may face, including perception security, planning security, communication security, and evaluation and testing security. In response to these security challenges, a series of technologies and strategies have been proposed, and comprehensive security measures have been adopted to effectively reduce the security risks of intelligent aviation ammunition systems and improve their credibility and availability in actual combat. This research provides theoretical support and practical guidance for the safety technology of intelligent aviation ammunition systems, and provides a reference for its future development and application.
Trajectory Tracking Control for USVs Based on Red-billed Blue Magpie Optimized ADRC
Authors: Luo Zhang Zi , Chongwei Han , Kai Shen , Qikai Hao , Jiayue Jiang , Ziyi Zhang 
Published: December 2024

Abstract
Aiming at the characteristics of the underdriven surface unmanned ship trajectory tracking problem such as strong coupling, nonlinearity, under-driving and strong disturbance, the mathematical model of the system is established by considering the problem of parameter uncertainty. Combining the Red-billed Blue Magpie Optimizer (RBMO) and the Line-of-Sight (LOS) guidance law and the Active Disturbance Rejection Control, the problem of mutual coupling between the states of the system is solved, and the decoupled two-channel Active Disturbance Rejection Control is formed. The optimization algorithm of the red-billed blue magpie is used to optimize nine parameters of the dual-channel Active Disturbance Rejection Control, which solves the problem of the numerous parameters of the Active Disturbance Rejection Control and the difficulty of parameter tuning. The simulation results show that the control method can enable the system to track the circular trajectory related to the time parameter under the condition of parameter ingestion and subject to external perturbation, with high tracking accuracy and anti-perturbation ability, which provides a new technical way for the research of trajectory tracking control of underdriven surface unmanned vessels.
Analysis on Fluid-Solid Coupled Heat Transfer of the High Speed Rocket Sled Slipper
Authors: Yang Yang , Zhang Chenhui , Liu Jin , Zhang Chuanxia , He Xiangfeng 
Published: December 2024

Abstract
In order to determine the heat transfer situation in the gap between the slipper and the rail in high speed rocket sled test, based on heat transfer theory and the calculation results of the steady-state flow field in the narrow gap between the slipper and the rail, coupled calculations were conducted on the flow field around the slipper and the slipper structure field. With a heating time of 0.1s, aerodynamic heat transfer results of the slipper structure with different gaps and different Mach numbers were obtained. The results showed that the temperature of the slipper increased with the increase of Mach number, and the thermal environment of the slipper was the most severe when the gap was 5mm and Mach number was 6, the internal temperature of the slipper gap was about 200K higher than the outer surface part, and only the outer layer material of the slipper, which is about 1mm thick, is significantly heated.