Abstract:The threat of chemical warfare agents is a major security threat to all countries around the world.In order to effec-tively respond to control various threats to national security emergencies and prevent the situation from further escalating,ensure the safety of our military and the health of the general public,maintain national security and economic development,long-range detec-tion,tracking and scanning and qualitative identification of shipborne chemical toxic gas reconnaissance devices is crucial impor-tance.It provides important guarantees for the response measures taken in the event of rapid emergency handling of various chemical weapons wars and sudden emergency incidents,maximizing the protection of the lives and mental health of our military personnel,and it is conducive to develop of the socialist modernization drive and the construction of national security protection.

Abstract:Real-time prediction of the trajectories of moving targets using neural network models has extremely high applica-tion value.Taking the dynamic trajectory prediction of drone as a case study,this paper studies the trajectory prediction of moving targets based on the reservoir computing algorithm.The reservoir network is trained with the acquired historical of drone,and the network model generated by the training is used to predict the future movement trajectory of drone.Based on the network model trained with 5 000 frames of historical movement trajectories of drones,real-time prediction of the next moment trajectory is achieved.According to the evaluation criteria for trajectory prediction,the experimental results show that when predicting the trajec-tories for the next 10 frames,the prediction errors of 80％of the prediction points can be controlled within 0.8％.

Abstract:Currently,the form of war is accelerating its evolution,and the concept of the U.S.military's chain of destruction is constantly being iterated and updated.Starting from the definition of the concept of the chain of destruction,the article summarizes and introduces the four development stages of the U.S.military's chain of destruction concept(embryonic stage,mature stage,cross domain stage,and prospect stage),and finally summarizes the development trends of the U.S.military's chain of destruction in four aspects.

Abstract:Cognitive radar,as an advanced radar system,is different from traditional radar in its excellent adaptive environ-ment ability,which makes cognitive radar used in many important and complex detection tasks at high frequency.Aiming at the un-certainty and noise interference in maneuvering target tracking,this paper proposes a method combining interactive multiple model(IMM)and unsensitive Kalman filter(UKF).By introducing IMM model based on the simplification of waveform library based on depth Q learning,the simplification of waveform library is realized,which effectively reduces the calculation time and improves the tracking accuracy.At the same time,the target tracking process is further optimized by using the insensitive Kalman filter(UKF),and the robustness and adaptability of the system are enhanced.The experimental results show that this method reduces the detec-tion error by 26.2％on average and shortens the calculation time by 60％on average.

Abstract:Unmanned aerial vehicles(UAVs)face technical challenges of insufficient information utilisation and sparse rein-forcement learning rewards when performing path planning tasks in complex dynamic environments.Existing methods typically rely solely on locally perceived information for path decisions,making it difficult to effectively utilize global prior information acquired before the task.This results in inefficient algorithm training and limited path planning performance.To address the aforementioned issues,this paper proposes an UAV dynamic path planning method based on reinforcement learning with prior information,which achieves effective integration of heterogeneous information by designing a mechanism for collaborative fusion of multiple information sources.The A*algorithm is employed to preprocess known static environmental information,generating global navigation pathways as prior guidance data.Real-time local fields of view are converted into RGB three-channel image representations to accurately per-ceive dynamic obstacle states.A four-channel tensor input is constructed,fusing global navigation information with local perception data across channel dimensions to provide rich environmental state representations for reinforcement learning algorithms.A path planning decision model based on Deep Q-Networks is designed,employing a convolutional neural network architecture to extract multi-channel fusion features.An innovative hierarchical reward function based on global guidance is proposed,utilizing navigation path information to construct continuous learning signals.This effectively addresses the issue of reward sparsity,significantly en-hancing the algorithm's convergence efficiency.Compared to traditional reinforcement learning methods,it demonstrates notable im-provements in both training convergence speed and path quality.The experimental results validate the effectiveness of the prior infor-mation guidance mechanism in path planning for complex dynamic environments,providing new technical insights for the advance-ment of UAV autonomous navigation technology.

Abstract:Low-temperature piezoelectric accelerometers are widely used in vibration and shock testing,fault diagnosis,and monitoring.These critical detection areas are important indicators of a nation's status as a space power.To address the issue of signif-icant sensitivity temperature drift in low-temperature environments,this paper designs an innovative low-temperature piezoelectric accelerometer.This sensor not only exhibits minimal temperature drift in low-temperature environments but also maintains excellent stability in high-temperature conditions.The sensor adopts a compressive structure consisting of a stack of four layers of PZT-5A piezoelectric crystals and one layer of LiNbO3 piezoelectric crystal.Its performance is optimized through ANSYS simulation,and the effectiveness of the proposed method for reducing sensitivity temperature drift is verified.The results show that,within the tempera-ture range of 300℃to-296℃,the sensitivity temperature drift of the sensor is less than±3.76%.Therefore,the sensor designed in this paper demonstrates excellent stability and high measurement accuracy in low-temperature environments.

Abstract:This paper introduces a robust Recursive Least Squares(RLS)algorithm,conducts a theoretical analysis of its ro-bustness,and discusses the influence of the initial values on the convergence performance of the algorithm.Theoretical analysis and computer simulations show that this algorithm has strong interference suppression ability and robustness.The robust RLS algorithm can be applied to space-time anti-jamming in satellite navigation.Compared with the classical Least Mean Squares(LMS)and Nor-malized Least Mean Squares(NLMS)algorithms,it has a faster convergence speed and stronger interference suppression ability.

Abstract:In order to prevent aircraft from crashing off the runway due to runway pollution at high-altitude airports,this paper is based on systematic bibliometric analysis,integrating domestic and foreign research on runway pollution friction degradation and core theoretical analysis of the advantages and disadvantages of three research methods:experimental,theoretical analysis,and nu-merical simulation.The evolution mechanism of the nonlinear degradation interface of aircraft runway friction performance under dy-namic load and environmental coupling of airport runway pollutants is analyzed.Research has shown that under waterlogged condi-tions,the contact stress on airport runways is negatively correlated with water film thickness,negatively correlated with speed,and positively correlated with tire load.Under snow conditions,the slip distance is positively correlated with velocity,positively correlat-ed with snow thickness,and negatively correlated with load.In the state of ice accumulation,frictional force is negatively correlated with velocity and positively correlated with load.Based on the research results,a theoretical basis is established for establishing a safety identification technology for runway pollution in high-altitude airports.

Abstract:In this paper,a flight path design method for aircraft combat test based on quantum genetic algorithm is proposed.This method uses qubits representing quantum superposition state to encode the flight path design of aircraft.A quantum gate adjust-ment strategy with quantum characteristics is introduced.The flight path is gradually optimized for traction for test purpose.The ex-ample shows that this method not only has good population diversity,but also can realize the core test purpose of aircraft test route design.

Abstract:As the most important equipment among underwater weapons in various countries,the study of acoustic scattering characteristics of underwater small vehicles has important theoretical value and wide practical application background.In order to achieve effective detection and identification of underwater small vehicles,it is necessary to have a profound understanding of their acoustic scattering characteristics.This article studies and analyzes the scattering echo characteristics of underwater small vehicles at different frequencies and incident angles.Through the prediction model of underwater target scattering characteristics and the sim-ulation of target echoes under high-frequency multibeam imaging cameras,it is analyzed that high-frequency imaging cameras have high-resolution detection capabilities for fine features of underwater small vehicle target contours.

Abstract:With the in-depth application of digital twin technology in the field of ships,taking into account the dynamics of various parameters of the ship operation environment,realizing the two-way data virtual-real interaction between the ship's physical operation process and its digital twin model is the key to construct a digital twin model of the ship's operation process.This paper pro-poses a virtual-real interaction method for digital twin model of ship operation process under dynamic environment,which utilizes TCP/IP,MQTT protocol and IoT cloud platform to solve the problem of virtual-real interaction of real-time dynamic environmental parameters,dynamic sensing data and dynamic feedback control data between ship operation process and its digital twin model,and realizes real-time transmission and dynamic mapping of"real-virtual"and"virtual-real"data.After experimental verification,the virtual-real interaction method has low data transmission delay and can dynamically respond to the environmental parameters of the ship′s physical operation process.The research results provide theoretical and methodological support to enhance the digitaliza-tion of ship operation process.

Abstract:Aiming at the actual demand of the combat method test and optimization of the surface ship formation in the actual combat training,through the study of the characteristics of the combat method experiment method,this paper puts forward the com-bat method experiment mode which fuses the simulation experiment and the counter-simulation method,designs the experiment en-vironment and the flow method to support the experiment mode,and carries on the practical application mode,and carries on the practical application to the experiment mode.It provides theoretical support and method guidance for carrying out combat method ex-periment in-depth.

Abstract:Radar calibration is the testing and evaluation of radar performance,and accurate calibration helps to correct sys-tem errors and improve radar measurement accuracy[1].The traditional calibration methods include static calibration and dynamic calibration.Static calibration is based on fixed tower calibration(passive calibration)or beacon calibration(active calibration),while dynamic calibration is a comprehensive method using navigation test technology[2,9,11].This article uses a standard RCS fixed wing drone equipped with RTK high-precision positioning equipment,and achieves calibration of radar performance parameters through high-speed long-distance communication,RCS adjustment,and radar position coordinate analysis and comparison.Com-pared with traditional calibration methods,this method has high calibration accuracy,good universality,convenient implementa-tion,and economic applicability,which is of great significance for improving radar support level and reducing radar support costs.

Abstract:Inertial MEMS devices require testing and calibration during the production,acceptance,and use stages.The most commonly used testing equipment is the turntable,but the accuracy of performance indicators such as angle and speed provided by traditional testing turntables is difficult to exceed that of similar performance indicators of the tested inertial devices.This article de-signs an upper computer software system for turntable control to address this issue.Using Visual Studio2017+QT5.12 as the upper computer software development platform,the software design is programmed by using VC++language under VS2017.The upper com-puter interface is built using UI controls and graphic design tools under the QT framework.To ensure real-time control,the RTX re-al-time operating system is adopted.The upper computer sends,receives,converts,and displays real-time control instructions for the turntable through the RS422 serial port.Tests show that the angular position error of the turntable control is less than 0.56%,and the angular velocity error is less than 0.23%.

Abstract:MEMS(Micro-Electro-Mechanical-System)gyroscopes are miniaturized inertial sensors that are widely used in navigation,missile guidance,autonomous driving,virtual reality,and UAVs.However,MEMS gyroscopes are often affected by noise from the environment and the hardware itself,which reduces their performance and limits the application of MEMS gyroscopes in high-precision applications.Therefore,signal denoising becomes one of the important means to improve the accuracy of MEMS gyroscopes.In this paper,a filtering algorithm is proposed based on local mean decomposition(LMD)and adaptive least mean squares(LMS).Firstly,the output signal of MEMS gyroscope is decomposed using robust local mean decomposition,and then the PF components are categorized into mixed components and useful components by applying the multivariate permutation entropy,and then the mixed components are denoised by LMS to reconstruct the output signal of MEMS gyroscope.And experiments are con-ducted to verify the proposed algorithm,and the experimental results show that the noise mean and noise variance are significantly improved.

Abstract:Underwater unmanned combat,as a new type of combat force,is increasingly receiving attention from the country and the military.Intelligent target recognition of underwater acoustic signals is an important technical support for achieving un-manned underwater combat.This article summarizes the current application status of underwater unmanned underwater equipment in various countries around the world,systematically sorts out the research status of underwater acoustic signal detection and recog-nition technology based on artificial intelligence technology,analyzes the military application scenarios of intelligent detection and recognition technology of underwater acoustic signals,and finally proposes suggestions on how to develop intelligent detection and recognition technology of underwater acoustic signals in China to enhance underwater unmanned combat capabilities.

Abstract:In recent years,the marine waste problem has become increasingly serious,posing a threat to water resources.To remove the waste and bring it back to shore,the use of the elevator becomes critical.However,the fork of the elevator often has structural problems due to repeated stress.In this study,SolidWorks software establishes a solid model for the lifting fork,carries out static strength and stiffness study using Ansys Workbench,and simplifies the deflection model through fatigue analysis conducted by nCodeDesignLife software.The results show that the maximum equal effect force of the fork is 292.48 MPa,the maximum deformation is 61.46 mm,the minimum fatigue life of the fork is located at the end,and the fork will not resonate at the design speed,and the dynamic characteristics meet the design requirements.Under normal working conditions,the amount of wear on the surface of the fork is small,but in the case of cargo jamming or overload,large wear will occur,and the maximum deflection calculated under real working conditions is about 4.5 mm.It provides an important reference for the design and development of similar boom structure.

Abstract:Unmanned ships,as autonomous water intelligent agents,have been widely used in high-risk tasks in complex ma-rine environments in recent years.Compared to static water environments,ocean wind and wave environments have more interfer-ence on the control of unmanned ships,making optimization of real time unmanned ship control increasingly important.This article establishes a dynamic model for the motion of unmanned ships in wind and wave environments,designs a control system combining PID control algorithm and LOS path control concept,and optimizes wind and wave compensation through an improved IGA-BP neu-ral network based on chaos algorithm.Finally,the simulation analysis of the control optimization effect is carried out through the un-manned ship motion simulation program,verifying the effectiveness of the control optimization.

Abstract:Gun barrel bending and muzzle vibration will seriously affect its firing accuracy,which is meaningful for the mea-surement of barrel bending and muzzle offset.According to different scenes,the measurement methods can be divided into the mea-surement of barrel bending in the static state,the measurement of muzzle offset in the static shooting state and the measurement of muzzle offset in the driving state.The measurement methods in different scenarios are summarized and reviewed,which can provide reference for the measurement methods of barrel bending and muzzle vibration.

Abstract:With the continuous progress of integrated circuit technology,the integration degree of chips is becoming higher and the process size is becoming smaller and smaller.As a result,the challenges faced by chip physical design are constantly in-creasing.In this context,the reasonable layout planning of macro units in design is particularly important.The traditional macro cell layout planning method has the problems of low efficiency and long time consumption,which not only complicates the design pro-cess but also often fails to meet the requirements of design quality.Although automatic layout planning can improve efficiency,it of-ten faces many challenges in practice,such as an increase in the number of physical rule violations,increased power consumption,and severe congestion.Aiming at the shortcomings of traditional layout and automatic layout methods,a memorywrapper combined with hierarchical physical design method is proposed to control the routing between macro units.Experimental data shows that com-pared to automatic layout and traditional layout,there is a significant improvement in timing results.The establishment time of the worst negative violation(WNS)is reduced by 1.63 ns and 1.41 ns,the total negative violation(TNS)is reduced by about 66％and 52％,the number of violations is significantly reduced,and power consumption is reduced by 17.3％and 10.4％.