For the mission scenario of using a cluster of patrol missiles to fight against the ground high-defense mobile targets, an attack decision-making process method based on the height division of patrol missiles with different operational functions is proposed. By deploying the YOLO X and VGG-16 integrated online target recognition and damage assessment network (YOLO-VGGNet) on any patrol missile, the patrol missile can autonomously strike a target based on visual sensing information only under no communication conditions. At the same time, a method for terminal reassessment and target attack point selection is proposed so that the patrol missile can not only reassess the ground target at the terminal of attack when flying to the target, but also locate the feature parts that have not been struck. The nodes of missile swarm can autonomously decide whether to continuely attack and prioritize the attack on the feature parts of target. Simulated results show that under no communication conditions, the proposed YOLO-VGGNet online damage assessment network can not only destroy the targets in the mission area completely, but also reduce the unnecessary ammunition loss and prioritize the attack on the functional parts of the target, which is expected to improve the overall combat effectiveness of missile swarm.

High-entropy alloys are applied in extreme environments,such as high-speed collision and explosive impact,due to their excellent comprehensive mechanical properties.To study the deformation,damage,and failure behaviors of high-entropy alloys under dynamic loading,HfZrTiTaAl-based refractory high-entropy alloys are designed and prepared.Quasi static compression experiment and split Hopkinson bar (SHPB) experiment are conducted on the high-entropy alloys,and the Johnson-Cook constitutive model parameters containing damage are obtained through numerical simulation.The damage evolution process and failure of material under dynamic loading are simulated.The results indicate that the refractory high-entropy alloy exhibits good plasticity under quasi-static compression conditions.Within the strain rate range of 0.001s^-1-3500s^-1,the HfZrTiTaAl-based high-entropy alloy exhibits a strain rate effect,with a yield strength increasing from 1140MPa to 1568MPa.Numerical simulation shows that the damage of specimen is mainly concentrated on the cross-section at 45° angle to the loading direction under high strain rate loading,and the damage degree of the elements in the middle of the specimen is greater than that of the elements on both sides of the specimen.As the loading strain rate increases,the percentage of elements with a damage degree greater than 0.8 in the total specimen elements gradually increases.

The effect of the divergent section contour of supersonic nozzle on the multiphase flow and propulsion performance of underwater rocket propulsion nozzle is studied by a series of numerical simulations.The underwater flow processes of conical and parabolic nozzles with different divergent section contours under still water,variable depth and over-expanded conditions are simulated.A numerical model of underwater supersonic gas jet is established based on the volume of fluid(VOF)multiphase model.The influence laws of the contour types and key parameters of divergent section on the nozzle near-field flow structure,flow separation characteristics,and thrust oscillation characteristics are analyzed.The results show that the separation shock structure in the nozzle operating in deep water is highly unstable.The gas-liquid separation may also occur at the separation point in the divergent section,and the nozzle thrust oscillates on the basis of the full-flow value.There is a dynamic transition of flow separation patterns in parabolic nozzles,and the gas-liquid separation phenomenon is not significant under the restricted shock separation(RSS)pattern.The effect of contour type is more pronounced than that of contour parameters,with the parabolic nozzles having more moderate thrust oscillations than the conical nozzles,and the difference is more significant at greater water depths.At a depth of 90 m,the maximum difference in average thrust of the nozzles with different contours reaches 10.13% of that of the basic parabolic nozzle.

Ceramics will undergo when operating under long-term high temperature and high stress conditions,which promotes the generation and propagation of cracks and eventually leads to failure.The evolution of creep damage is closely related to the microstructure of ceramics.Establishing a microscopic finite element model of ceramic materials is conducive to a more in-depth understanding of this relationship.For this purpose,a dynamics-based 3D crystal deposition model is proposed by taking Si₃N₄ ceramics as the research object.The sintering process of Si₃N₄ ceramics is simulated by the Monte Carlo Potts crystal growth model,striving to reproduce the dynamic growth process of Si₃N₄ ceramics as well as the microstructure characteristics,such as the size,shape and orientation distribution of crystals after crystallization,and the size,shape and distribution of pores.Based on the geometric boundary description generated by this simulation,a Python script is automatically generated to complete the finite element modeling in a FEA software.The statistical elastic constants of Si₃N₄ ceramics are verified using this finite element model.By comparing the calculated results with the experimental data,it is shown rhat the relative error is approximately 4.5%,which shows a good agreement.

The magnetic gradient tensor system (MGTS) is the application basis for detecting the full tensor gradient field of magnetic target. Regional magnetic anomalies leads to a magnetic gradient tensor field, which MGTS uses as an information source to achieve the magnetic gradient tensor measurement through the differential calculation between vector magnetic sensors. Compared to the magnetic total field and vector field detection equipment, MGTS has high resolution, large information content, and strong anti-interference ability, which can obtain more potential physical property information of targets. The progress of target detection technology based on MGTSs worldwide is studied to provide theoretical reference and technical support for the modernization and informatization construction of magnetic detection equipment in China. The development process and stages of modern magnetic detection technology are elaborated, and then the two types of MGTSs based on superconducting technology and flux gate method, as well as their applications areintroduced and summarized. The calibration, compensation, noise reduction, magnetic target positioning and recognition technologies of MGTSs are reviewed. Finally, the design ideas for future high-precision magnetic gradient tensor detection instruments are prospected, and the current problems and development trends of various key technologies for magnetic gradient tensor detection are summarized.

The near-field blast wave characteristics(0.06m/kg^1/3<Z<1m/kg^1/3)of typical charge structures(sphere and cylinder)are investigated for supporting the protection of sympathetic detonation through near-field explosion experiment and numerical simulation.The load of near-field blast wave of cylindrical charge is experimentaly investigated,and a numerical caculation model of near-field explosion is established,The near-field blast wave structures and spatial distributions for peak parameters of spherical and cylindrical charges are analyzed.The results show that the near-field explosion loads are affected by the detonation products,and the reflected overpressure histories exhibit multi-peak and jagged form,which are due to the effects of multiple reflection waves and the complex flow of detonation products after the Mach region.The inverse of pressure and density gradient between detonation products and shock wave interface leads to the Rayleigh-Taylor instability effect.The generated interfacial microjets of detonation products promote the complexity of multiple reflection wave structure,resulting in large uncertainty of measured results of near-field reflected loads.The near-field incident overpressure curve shows a typical double-peak structure,which comes from the air shock wave and detonation products.Due to the structural effects of charge,the incident wave loads of cylindrical charge distribute non-uniformly along the axial and radial directions.The spatial distribution of incident parameters drastically varies when the azimuth angle is between 30°-60° because of the bridging wave effects.The modified predictive model can capture the incident peak overpressure and impulse in the range of 0.06m/kg^1/3<Z<1m/kg^1/3 for cylindrical charge(L/D=0.8)with center initiation under arbitrary azimuth angle,and the relative deviations among predicted results and numerical results of incident peak overpressure and impulse are less than 20%.

Recoilless launch can reduce recoil effectively,but it causes the reduction in low muzzle velocity.To resolve the conflict between muzzle velocity and recoil force,a reverse jet recoilless scheme based on combustible cartridge and electromagnetic induction ignition is proposed.Energy is transmitted to the ignition head through electromagnetic induction,which ignites the fast-burning propellant and combustible cartridge in the chamber to provide the launch energy.After reaching a certain level of pressure,the Laval nozzle is opened,and gas is sprayed backwards to balance the forward impulse of projectile.For high muzzle velocity recoilless launch,the loading density and chamber pressure are high,and the interior ballistics is prone to instability.To increase the stability of interior ballistics,a classic interior ballistic model during the shooting process is established,The performance of interior ballistics is analyzed and the stability of interior trajectory is studied through numerical simulation.The ballistic test research was conducted to verify the proposed scheme.The results show that the proposed scheme.can be used to reduce the recoil impulse to below 1.5N·s without reducing the muzzle velocity of projectile.The scheme can provide new ideas for the future development of light weapons.

The surface absorptivity of optical elements is the main factor causing the abnormal temperature rise under continuous laser irradiation. Research has found that the surface absorptivity of optical elements is influenced by multiple factors and exhibits nonlinear variations. Therefore, a concept of equivalent surface absorptivity is proposed to characterize the comprehensive absorption performance of optical elements for laser. Firstly, a finite element model of the optical element irradiated by Gaussian continuous laser is established to simulate the temperature rise process of optical elements under laser irradiation, and a laser irradiation effect experimental system is established. The surface absorptivity, surface morphology and temperature rise process of surface center point of optical elements are measured, tested and analyzed, and the correctness of the prtoposed model is verified by the experimental results. Based on the experimental results, the model parameters are adjusted to obtain the equivalent surface absorptivity of the optical element for laser. The research show that the simulated error of equivalent surface absorptivity is smaller and its simulated precision is higher compared with the measured surface absorptivity, The research results provide reference for the state monitoring of optical element and the pollution prevention and control.

Amphibious aircraft inevitably suffers from the hydrodynamic impact of waves and other complex sea conditions during taxiing on the water surface, and in serious cases, the fuselage structure may be deformed and destroyed, which threatens the safety of airframe and aircrew. The structured arbitrary Lagrange-Euler (S-ALE) method is used to investigate the hydrodynamic response of amphibious aircraft during taxiing on a wavy water surface by taking a domestic large-scale amphibious aircraft as the research object. A coupled fluid-structure simulation method based on S-ALE and penalty function contact algorithm is established, and a numerical wave pool is generated and simulated by using the physically imitated push-plate wave-making mode and the mass-damped wave dissipation method, and the hydrodynamic characteristics and wave resistance of the aircraft during taxiing on calm and wavy surfaces are investigated, respectively. The results show that the S-ALE method can effectively simulate the dynamic response of amphibious aircraft taxiing on water surface; the attitude angle of aircraft taxiing at a steady speed of 19.4m/s under a wave height of 1.2m is 7°, and the corresponding resonance wavelength is two or three times of the fuselage length. When the ratio of the airplane fuselage to the wavelength is 1, the vertical overload becomes bigger and bigger under the environment of 1.8m wave height, and will gradually converges under the wave height of 1.2 m. While the changes of the wave height have no obvious effect on the pitch and heave of the aircraft.

In a complex communication environment,it is difficult for the global navigation satellite system (GNSS) to provide users with stable and accurate location information.An enhanced adaptive genetic location (EAGL) algorithm is proposed to solve the problem of positioning bias caused by the uncertainty of measured data.In the proposed algorithm,a localization model based on time difference of arrival is established to reflect the relationship between the location of target source and the signal environment.The possible solutions satisfying the objective function are encoded in real number,and the fitness function is established,which is used to calculate the fitness value of each individual.The selection operation is performed on the population,and the improved adaptive crossover and mutation operation are used to improve the genotype quality of the population and avoid falling into the dilemma of local optimal solution.The genotype of the individual with the highest fitness value is obtained by iteration to get the exact coordinates of a target source.The simulated results show that the positioning accuracy of the proposed algorithm is higher than those of the simple genetic algorithm (SGA) and Chan-Taylor algorithm.With the gradual increase in the error of the measured value,the error fluctuation of EAGL algorithm under different error conditions is the smallest.As a result,EAGL algorithm is stable and capable of achieving the high-precision positioning.

Ducted-fan unmanned aerial vehicle(UAV)can realize take-off,landing and hovering,which has a simple and reliable structure and high safety coefficient.In view of the restricted take-off and landing environment,redundancy of folding wing mechanism and low aerodynamic efficiency of conventional water-air amphibious UAVs,a new dual ducted fan water-air amphibious trans-media UAV is designed.The fuselage is optimized with reference to underwater unmanned underwater vehicle(UUV)and UAV,and the tilting dual ducted fan power system is used.Based on the improved blade element momentum theory,the theoretical formula for calculating the lift of small ducted fan is derived.The structural vibration pattern of UAV as a whole under the mechanical physical field is analyzed,and the aerodynamic performance of the whole vehicle is verified by flow field finite element simulation.The analyzed results show that the calculated results of the improved small ducted fan lift calculation formula are basically consistent with the wind tunnel test results,with the maximum error between the two results is 4.5%,and the designed small ducted fan can provide 20% of additional lift with high aerodynamic efficiency.Through the modal analysis,it is found that the UAV fuselage structure is more stable,and the vibration pattern is reasonable.The wind tunnel test and the test flight verification of the ducted fan water-air amphibious UAV in different media are completed,which proves the feasibility of the UAV design scheme.

The launching mechanism of submarine-launched missiles under polar underwater environments is studied. The Arbitrary Lagrangian-Eulerian (ALE) method and the smoothed particle hydrodynamics (SPH) method are used to establish the fluid and infinite whole ice models, respectively, based on finite element software, and a plastic compressive tension material model with equation of state is used to reproduce the sensitivity of mechanical properties of ice to stain rate. Consequently, a fluid-structure interaction model of underwater vehicle ice-breaking is built. Based on the validated key numerical methods, the load and collision force characteristics of underwater vehicle under different ice thicknesses and vehicle speed conditions are investigated by simulation. and the stress distributions of vehicle and ice in the process of ice-breaking are analyzed. The result shows that the load and interaction time imposed on the vehicle increase with the increase in ice thickness, and with the increase in vehicle speed, the loads imposed on the vehicle increase, and the interaction time decreases.

At present,the traffic management relying on manpower is characterized by inaccurate statistics and delayed feedback.A vehicle detection algorithm based on the improved YOLOv7-tiny algorithm suitable for deploying on edge terminal devices is proposed to better protect people’s lives and property.A deep powerful residual (DP_Res)convolutional block isconstructed to perform the lightweight improvements on the efficient layer aggregation network-tiny (ELAN-T) module of backbone network.By reducing branches,the lightweight improvement on the ELAN-T module of the feature fusion network is made to reduce the number of parameters and computational load of the network,and the structure of the feature fusion network is reconstructed;The efficient channel attention mechanism and the EIOU bounding box loss function are introduced to improve the accuracy of the algorithm.The experiment is conducted on the preprocessed UA-DETRAC dataset,and the parameters of the improved algorithm are reduced by 15.1% compared to those of the original YOLOv7-tiny,with a reduction in computation of 5.3% and an increase in mAP@0.5 of 5.3 percentage points.The experimental results show that the improved algorithm not only achieves lightweight,but also improves the detection accuracy,making it suitable for deployment on edge terminal devices to complete the task of detecting vehicles on the road.

The route planning issue for island and reef cruise based on the dynamic collaboration among ships and drones is investigated to enhance the efficiency of maritime cruise. The issue is characterized by the dynamic cooperation among ships and drones, the precise spatio-temporal coupling, and the simultaneous optimization of discrete and continuous variables. Accordingly, a mixed-integer second-order cone programming model to minimize the mission completion time is developed for a combined optimization of the ship navigation path, the drone flight trajectory, and the drone takeoff and landing positions. The adaptive large neighborhood search (ALNS) algorithm is applied to design three destruction operators, two repair operators, and their adaptive mechanisms. A case analysis is conducted based on data from several islands and reefs in a specific maritime area, demonstrating that the cruise time can be reduced by more than 45% under the ship-drone coordination mode. The computational results indicate that the ALNS algorithm can solve the instances with up to 80 islands and reefs within 90 seconds, significantly outperforming the CPLEX solver and the two-stage heuristic algorithm in terms of solution quality and efficiency. The proposed route planning method for island and reef cruise based on ship-drone collaboration provides a reference for efficiently conducting maritime rights protection and law enforcement missions.

In order to improve the confidentiality of physical layer of wireless communications,a dynamic double-layer generalized multi-parameter weighted fractional Fourier transform (DDL-GMPWFRFT) secure communication method based on two-dimensional Hénon-cosine-exponent (2D-HCE) chaotic encryption is proposed.A 2D-HCE hyper-chaotic map is constructed by introducing the cosine and power-exponent nonlinear terms to Hénon map,which is verified by using Lyapunov exponential and system bifurcation diagram.64 kinds of double-layer GMPWFRFTs (DL-GMPWFRFTs) are designed and constructed based on GMPWFRFT and double-layer WFRFT structure.2D-HCE chaotic sequence is used to implement the amplitude-phase encryption of constellation,and DL-GMPWFRFT transformation type is randomly selected for the dynamic transformation encryption of constellation to further conceal the modulated signal style and improve the system’s defense ability against parameter detection.Simulated results show that the constellation map encrypted by the proposed method presents a Gaussian distribution,and the keyspace is up to 2²¹⁸.The key sensitivity is much higher,and the bit error rate of eavesdropper is still in the range from 0.4 to 0.5 even if there is an error of 10^-15 between the key from an eavesdropper and the correct key.

The influence of the free surface on the shape of supercavitation and the hydrodynamic characteristics of underwater vehicle is investigated. The motion process of a supercavitating vehicle near the free surface is numerically simulated using an adaptive mesh method and the volume of fluid method, and the effects of the free surface on the shape of supercavitation, and the lift, resistance and torque of the vehicle are analyzed. The research findings show that the presence of the free surface causes the tail of supercavitation to shift away from the free surface, resulting in the length of supercavitation being significantly shorter than that in an infinite water domain. The velocity of vehicle has a significant impact on its lift near the free surface. When the velocity of vehicle is less than 50m/s, its lift is negative; when the velocity of vehicle exceeds 60m/s, its lift becomes positive. When the vehicle is fully enveloped by the cavitation, only the cavitator head is wetted. A zero-torque point always appears near the front (x=1D, where D is the diameter of the cavitator) of the vehicle. When the interface of supercavitation tail intersects with the vehicle, it causes a change in the position of the zero-torque point. When the attack angle of the vehicle is positive, the torque is less affected by the water depth. However, when the attack angle is negative, the torque is significantly influenced by the water depth.

To verify the feasibility of equipping electric field sensors on fast motion platforms to detect ship targets, this manuscript analyzes the mechanism of background electric field generation on fast motion platforms, builds a high-speed motion platform electric field detection system based on surface speedboats, and conducts real boat sea measurement experiments. The background electric field of speedboats at different positions, engine operating conditions, and sailing speeds is measured, and the measurement results are analyzed. By analyzing the experimental data at sea, it is found that the background electric field comes from: 1) the motion induced electric field of the speedboat platform, 2) the corrosion and electromagnetic radiation of the speedboat itself. The background electric field has high energy in the frequency band below 1Hz (static electric field), so the ship’s electrostatic field is not suitable as a detection signal source. When the speed is below 20 knots, the background electric field spectral density of the speedboat detection platform in the 1-30Hz frequency band is about 0.4μV/$\sqrt{\mathrm{Hz}}$, so the ship’s shaft frequency electric field can be used as the target signal source. To verify the feasibility of electric field detection on the speedboat platform, electric field targets of 100A·m were detected at different speeds of 5~15kn, with a detection distance of 1500m. Therefore, it is practical to use a fast motion platform equipped with electric field sensors to detect the ship’s electric field.

The traditional detection methods have the disadvantages of inaccurate feature extraction and low detection efficiency when processing the complex and dynamic flight trajectory data with real-time change in data length. An proposed flight trajectory anomaly detection method using the gradient-based optimization of long short-term memory network and support vector data description model based on gradient training algorithm optimization (LSTM-GBSVDD)is proposed. The LSTM network is used to extract the key features of variable-length flight trajectories and convert them into a fixed-length sequence representation. A multidimensional hypersphere classifier is constructed using the SVDD algorithm, which is used to model the normal flight trajectories and identify the potentially abnormal flight trajectories. To further improve model performance, a gradient-based training algorithm (GB) is introduced to jointly train the parameters of LSTM and SVDD, which greatly improves the detection accuracy and computational efficiency. The simulated results show that the proposed flight trajectory anomaly detection method using the gradient-based optimization of long short-term memory network and support vector data description model based on gradient training algorithm optimization (LSTM-GBSVDD) has good effectiveness and superiority in dealing with complex and changeable flight trajectory anomaly detection tasks, and has good application prospects.

The control strategy of traditional tank bidirectional stabilizers is difficult to effectively deal with the coupling,nonlinearity and uncertainty in the new generation of all-electric bidirectional stabilizers,while the model-based nonlinear control can make full use of a priori information from the dynamic model of the system to enhance the control effect.Based on this,an electromechanical coupled dynamics model of an all-electric bidirectional stabilizer taking the actuator dynamics into account is established,and a nonlinear sliding mode control method based on neural network compensation is proposed.The sliding mode surface and the improved sliding mode robust control law based on hyperbolic tangent function are introduced to construct the nonlinear sliding mode control function,which can effectively eliminate the system oscillations and improve the system stability.Meanwhile,the multilayer neural network technique is deeply fused to accurately estimate the uncertainty in the system and make compensation for the feedforward,thereby avoiding high-gain feedback.It is rigorously demonstrated by the stability theory based on Lyapunov functions that the proposed control strategy can achieve the asymptotic stability performance of tank all-electric bidirectional stabilizer with continuous control inputs.A co-simulation environment and a semi-physical experimental platform are built.The superiority of the proposed control strategy is verified through a large number of comparative experiments.

With the progress of information technology,the human-computer interaction modes of armored vehicles have been constantly updated.In order to explore the mission performance of occupant in new multimodal interaction and guide the design of multimodal operation and warning modes of future armored vehicles,a human-computer interaction experimental system which integrates multimodal interaction and adjusts the environmental load is established.Based on this system,20 adult males were recruited to conduct an ergonomics experiment involving operation (input) mode,warning (output) mode and environmental load level. The simulated target hitting experiments under different experimental conditions are performed in a virtual experimental platform,and the mission performances of occupants are analyzed.The experimental results show that the reaction time of occupant under mechanical input is the shortest and the operation error rate is the highest compared with those under touch input or voice input.The mission completion time of occupant under mechanical input and touch input is much shorter than that under voice input.Compared to the single modal visual (V) warning,the visual+tactile (V+T) dual-modal warning superimposed with tactile warning is used to significantly shorten the reaction time of occupant,while the visual+auditory (V+A) dual-modal warning superimposed with auditory warning has no significant difference.V+T or V+A dual-modal warning can significantly shorten the mission completion time of occupant.For the influence of environmental load,the mission performance of crew under low environmental load is significantly higher than that under high environmental load.As the environmental load increases,superimposing A warning in V+T warning will cause warning redundancy.The research can provide a practical basis for the design of multi-modal human-computer interaction in the armored vehicle cabin.

With the widespread application of composite cylindrical shells in engineering fields such as missile launch and submarines, the random vibration caused by random loads has gradually become an important consideration for their dynamic design optimization. The first-order shear shell theory and Hamilton’s principle are used to construct the motion control equations of the shell, and the boundary conditions are applied by artificial virtual springs. The pseudo-excitation method and the reverberation-ray matrix method are used to separate the non-homogeneous excitation equations in the generalized solution vector, and the unified matrix column formula under base acceleration and random load excitation is derived to complete the stochastic dynamic analytical modeling and solution of composite cylindrical shells. The calculated stationary/non-stationary random response results are compared with finite element simulations to prove the effectiveness of the proposed analytical model. On this basis, a series of engineering examples for power spectrum are developed to reveal the influences of shell thickness-to-diameter ratio, orthogonal anisotropy ratio and number of plies on the random vibration response of cylindrical shell.

Individual equipment is closely related to the user’ height and weight as well as the task division,and has the characteristics of high customization and mass production,which bring great challenges to the production and delivery of individual equipment.Packing is the last step of individual equipment production,and is also the most difficult step to achieve customization and mass production.To solve this problem,an optimization method of individual equipment packing production process based on digital twin is proposed.First,a digital twin model of individual equipment packing production process is established.Secondly,the whole production process of individual equipment,such as distribution and replenishment,transportation,workers’ picking and boxing,is described by simulation,and the production process is optimized by an improved genetic algorithm.Finally,the proposed method is actually applied in equipment production.The optimal design and optimization of the cargo location,the number of workers and the number of handling equipment are realized by iterative simulation,thus effectively solving the problems of highly dynamic,congestion and transportation conflicts,long production cycle,unbalanced busy and idle stations,and delayed detection of misloading in large-scale customized production of individual equipment.The production efficiency of soldier equipment packing is improved by 11.18%.

The current radar jamming decision-making method based on reinforcement learning sets the exploration rate parameter according to a single factor and fixed law,which leads to the increase in the number of confrontation rounds required for algorithm convergence.A reinforcement learning-based radar jamming decision-making method with adaptive setting of exploration rate is proposed.Based on the Metropolis parameter adjustment criterion of simulated annealing method,an adaptive setting criterion of exploration rate is derived from the number of radar operating states recognized by jammers,the number of jamming successes,the change rate of algorithm convergence curve and the jammer’s cognition of radar in the process of countermeasures.According to the effectiveness of jamming action,a jamming action space clipping strategy is designed to reduce the dimension of jamming action space and further improve the convergence speed of the algorithm.In the simulation experiment,two different radar working state diagrams are designed and compared by using the Q-learning algorithm.The simulated results show that the proposed method can achieve the adaptive setting of exploration rate when the radar working state transition relationship changes.Compared with the exploration rate setting scheme based on simulated annealing method,single factor and fixed law,the number of confrontation rounds required for the convergence of the proposed method in the two state diagrams is reduced by 18%,26%,45% and 42%,44%,48%,respectively.At the same time,it can also obtain greater benefits and higher jamming success rate,which provides a new idea of exploration rate setting for multi-functional radar jamming decision-making based on reinforcement learning.

Operational command decision-making is the core content of joint combat activities and the key to the success of joint combat action. The paper aims to fundamentally solve the problem about “weak command and decision-making ability of combined formation” from the perspective of cognitive psychology. Based on the systematic analysis of formation operational command decision-making, the bounded rationality decision-making is scientifically mapped from the perspective of dual-process theory. A function model of bounded rationality decision-making is constructed, the types of bounded rationality decision-making are divided, and the cognitive deviation of bounded rationality decision-making is identified. An intelligent hybrid decision-making framework is proposed for warship formation operational command. The strategy of enhancing the bounded rationality decision-making ability of formation operational command is formed. The results show that the research framework is scientific, effective and intelligent, and can greatly improve the commander’s operational decision-making ability. It provides strong theoretical and technical support for the development of the next generation auxiliary decision support system.

With the escalating complexity of modern naval warfare, the role of carrier-based aircraft in intricate battlefield environments has become increasingly prominent. The collaborative optimization of task assignment and ammunition configuration for carrier-based aircraft is studied to enhance the combat effectiveness and resource utilization efficiency of carrier-based aircraft and alleviate the burden on commanders in formulating the combat plans. The key elements in the collaborative decision-making process are systematically analyzed. Then, an integrated optimization model of carrier-based aircraft task assignment and ammunition configuration is established by taking the maximized mission benefit, minimized destruction cost of carrier-based aircraft and minimized ammunition cost as the optimization objectives. Furthermore, a fitness-based adaptive global artificial bee colony algorithm is developed by combining the characteristics of the model for model solving. The simulated results demonstrate the effectivenesses of the proposed model and algorithm, and that they can significantly improve the mission benefits of carrier-based aircraft while reducing the operational costs. The research results can provide theoretical reference and decision-making basis for the development and improvement of carrier-based aircraft combat plan.

The high-power diesel generator set for vehicles is the power supply unit of heavy-duty series hybrid electric vehicles.Due to the coupling effect of the torque impact of multi-cylinder diesel engine crankshaft and the electromagnetic torque pulsation of generator,the system torsional vibration phenomenon is prominent,and the dynamic quality is poor.A dynamic model of engine-generator set considering the electromechanical coupling effect is established,and the influence of electromechanical coupling effect on the inherent characteristics of the system is analyzed.The law of variation of torsional vibration characteristics with the parameters,such as electromagnetic stiffness,rotor eccentricity and torsional damper stiffness,is revealed.The dynamic response characteristics of the system under the combined excitation of engine and motor are analyzed,and the order of the main response is clarified.A torsional vibration control overall framework based on dual loop decoupling is proposed to solve the problem of excessive low-frequency torsional vibration response.An independent modal space optimal controller for the start stop process and an adaptive filtering compensating controller for steady-state operating conditions are designed for the suppression of engine main harmonic disturbances,and are verified through simulation.The results show that the proposed torsional vibration active control algorithm can achieve the suppression of main harmonic torsional vibration in the full speed domain of the engine.

The cavity flow and ballistic characteristics of a supercavitating projectile with tail fin under the action of transverse flow when entering water are studied. The vertical water-entry process of the projectile under the transverse flow interference is numerically simulated by using the overlapping mesh technique and a 6DoF dynamic model, and the influence of transverse flow on the cavitation shape, hydrodynamic characteristics and trajectory characteristics of the projectile during water entry is analyzed. The results show that the effect of transverse flow restricts the radial development of cavitation bubble on the windward side, but promotes the expansion of cavitation bubble on the leeward side, shifting the overall cavitation shape along the flow direction. As a result, the wetted area of the windward shoulder and tail fin of projectile is increased, and the distribution of the pressure field near the wetted area is changed, making the amplitude frequency of the projectile’s tail fin beat much greater than that in the absence of transverse flow. The intensification of tail fin beat motion inside the cavitation bubble increases the lift/drag coefficient and accelerates the attenuation of the projectile velocity. Besides, the deflection of cavitation shape along the flow direction causes the swing amplitude of pitch angle on the leeward side to be greater than that on the windward side, thus resulting in a deviation of the ballistic trajectory. Therefore, the transverse flow significantly impacts the trajectory and attitude angle of projectile.

In order to effectively identify the visual and auditory channel workloads of operators during the operation of a special vehicle, a machine learning-based workload recognition model is constructed from the electroencephalogram (EEG) signals acquired in a simulated driving environment. A total of 30 participants were recruited for experiment, and the visual and auditory workload states were induced by increasing the scenario complexity and administering an auditory N-back task. The experimental results show that, the power spectral densities in δ, θ, and α bands in the frontal lobe, δ and θ bands in the temporal lobe, θ band in the occipital lobe, and all four frequency bands in the parietal lobe under the auditory workload condition are significantly higher than those under the visual workload condition. Moreover, the brain network has a stronger connectivity at θ and β bands under the auditory workload condition exhibites. Notably, the θ-band power spectral density (PSD) emerges as the most effective feature for the identification of visual and auditory workload channels, enabling the random forest algorithm to achieve a maximum classification accuracy of 95.68%. Shapley additive explanations (SHAP) analysis indicates that the frontal lobe contributes most significantly to the classification outcomes. These findings demonstrate the effectiveness of EEG-based indicators in identifying the visual and auditory channel workloads, providing a theoretical foundation for the development of adaptive interaction systems.

Pressure field caused by ship sailing is an important information in the ocean battlefield. When a ship sails against waves, the pressure fluctuation caused by ship-wave interaction becomes the background interference of ship’s own hydrodynamic pressure field, which affects the accurate prediction and identification of ship target. Therefore, a fast algorithm for the target characterization of pressure field caused by ship sailing against regular waves in the shallow water is studied. The theoretical and numerical methods of pressure field suitable for regular waves environment and full speed of ship are established by using the wave source term method and moving pressure term method on the basis of shallow-water wave theory. Meanwhile, a fast and efficient numerical algorithm is developed, and an algorithmic program is compiled, which can simulate the regular waves in shallow water, the ship hydrodynamic pressure field in static water, and the temporal and spatial variation characteristics of pressure caused by ship sailing against waves. Based on the validation study, the characteristics of pressure field caused before and after a ship encounters with waves as well as the distribution characteristics of pressure at subcritical or supercritical speed are compared and analyzed, and the influence of waves interference on the temporal and spatial variation characteristics of pressure is revealed, which provides the theoretical basis and technical support for the prediction and identification of ship target under the disturbance of waves environment.

The traditional loop closure detection algorithm relies on the accuracy of odometer and the external global positioning information,which consumes too much computing resources,and the existing lightweight loop closure detection algorithm has poor translation invariance and difficulty in adapting to the sparse environmental characteristics in off-road road environment.In order to improve the positioning capability of unmanned platform in the condition of satellite rejection for a long time and a large range of tasks,a lightweight loop closure detection algorithm using light detection and ranging (LiDAR) point clouds to describe the ground feature is proposed.It is different from extracting the point cloud features from single or multi-frame point clouds by deep learning.And a global descriptor is constructed.The fast LiDAR point clouds ground feature description approach is used to achieve the fast feature extraction of single frame point cloud and the globally consistent position feature description,and the multi-frame LiDAR point clouds ground features are aggregated into the sub-map loop closure detection descriptors.A lightweight global descriptor is constructed by odometer pose between adjacent frames,and the global descriptors are matched and the loop closure detection is realized without prior position information.The proposed algorithm is verified by using the mechanical and solid-state LiDAR in off-road environment.Compared with the existing lightweight loop closure detection algorithms,the proposed algorithm has the advantages of high recall rate,good real-time performance and less resource consumption in the off-road environment.

Effective range is one of the most important performance indexes of supercavitating projectiles, which is influenced by the coupling of shape and weight parameters. In order to increase the effective range of supercavitating projectile, a numerical model for calculating the effective range of supercavitating projectiles is established, and a combination of four factors and five levels is designed according to the principle of orthogonal test design. The effective range data set of supercavitating projectiles under the influence of shape and weight parameters is obtained by simulation calculation, and an optimization method of design parameters of supercavitating projectiles is established by using BP(back propagation) neural network method and genetic algorithm, and the maximum effective range of supercavitating projectile and its corresponding shape and weight parameters are obtained. The results show that the underwater trajectory of supercavitating projectile has a stable tail beat characteristic. The mass has the greatest impact on the effective range through range analysis In the absence of precise mathematical model, the accuracy of the effective range prediction model trained by BP neural network based on limited data points is high with the average error of 0.735%. The optimal range of the whole domain under the influence of four-factors coupling is obtained by genetic algorithm. The range is improved by 5.01% compared with the best result of data set, and by 1.95% compared with the result of orthogonal optimization. The research results can providereference for the overall design of supercavitating projectile.

For the inaccurate track cost estimation in task allocation for multi-agent systems, a track cost calculation method based on extended rapidly-exploring random tree is proposed to rationally plan the motion trajectories of agents and improve the accuracy of track cost estimation. In order to solve the problem of premature contracting of dominant agents in improved contract net algorithm, an agent bidding transformation mechanism is proposed to make the dominant agents participate in the task bidding for multiple times and achieve the balance of task load between agents in a system. The simulated results show that the proposed track cost calculation method can be used to accurately calculate the trajectory between agent and target, and the trajectory between target and target. The agent bidding transformation mechanism solves the resource waste caused by the premature contracting of dominant agent, and the time of the agents to complete all tasks is reduced by 6.54%. However, when dealing with the dominant agent problem, the new mechanism will increase the bidding rounds of the entire task allocation.

The water-exit process of supercaviting projectile often has an angle of attack due to launch perturbation, cross-current, wave, etc., which affects the trajectory of projectile and interferes with the successful water-exit of projectile. A numerical model for the water-exit process of supercaviting projectile with angle of attack is established based on the volume-of-fluid multiphase flow model and the moving computational domain method, and the water-exit processes of projectile at with different angles of attack and velocities are simulated. The calculated results show that a small angle of attack of the projectile has little effect on the cavity morphology in the water movement stage, and the shoulder and tail of projectile are wet with the increase in the angle of attack. A secondary cavity produced by the shoulder wetting may be rewrapped in the tail, and leads to the longer and obvious asymmetric radial distribution of cavity on the inflow side. The cavity has a tendency to expand after the projectile penetrates the water surface. The surface pressure of projectile is high at the beginning of the movement, and the high pressure region decreases rapidly as a cavity is generated, and the local high pressure occurs in the subsequent movement due to local wetting, cavity closure near the water surface, and splash collision with the water surface. The local high pressure occurs mostly on the inflow side, resulting in a higher pressure on that side than on the backflow side. The lateral forces and yaw moments appearing on the projectile cause the trajectory and attitude of the projectile to change and the angle of attack to decrease. The larger the initial angle of attack is, the more obvious its effect on the angle of attack, yaw angle change and trajectory of projectile is. When the projectile is at an angle of attack of 5° and the velocity continues to increase, the effect of velocity on the cavity morphology, the motion trajectory and yaw angle of projectile is weakened.

Global navigation satellite system(GNSS)users are often located far away from satellites,making them vulnerable to intentional interference and deception.Ensuring the provision of high-precision navigation positioning,speed measurement,and timing information to GNSS users becomes a primary task in navigation warfare.To enhance the anti-jamming capability of user-ends and promote the development and maturity of their anti-jamming technologies,there is an urgent need for doing research on user-end anti-jamming performance evaluation.The existing studies have mostly focused on single-type indices or specific application scenario when researching user-end anti-jamming algorithms and analyzing their performance,resulting in insufficient research on related evaluation.Therefore,this study sets up typical application scenarios,such as single-antenna anti-jamming,array antenna anti-jamming,inertial navigation-assisted anti-jamming,and unmanned aerial vehicles(UAV)anti-jamming,and establishes a user-end anti-jamming performance evaluation index system based on multi-application scenario.Furthermore,the method including availability(A),dependability(D)and capability(C)is introduced to improve the limitations of existing models,and a sub-item performance evaluation method for user-end anti-jamming is proposed.Finally,based on fuzzy comprehensive assessment approach and improved combination weighting method that enhances the existing weighted product models,a comprehensive capability assessment method for user-end anti-jamming is proposed.The experimental results show that compared with the existing models,the proposed method is used to realize the serial connection of multiple models in the application scenario,reduce the tracking threshold of receiver to less than 45° and the dynamic measurement error rate to less than 1,and increase the array gain to no more than 36 dB,thereby improving the accuracy of the performance evaluation of the measured equipment and its ability to resist jamming threats.It can effectively assist the upgrade and performance optimization of the equipment in multiple application scenarios.

In order to improve the obstacles avoidance ability of intelligent vehicles,an trajectory planning and control method of intelligent vehicles is proposed based on velocity obstacle model.The proposed method is used to establish a velocity obstacle model for intelligent vehicles by combining the velocity obstacle method and obstacle expansion method,The motion uncertainty of dynamic obstacles in the velocity space is transformed into the positional uncertainty,and the safety margin is adaptively adjusted by obstacle size and relative velocity.To balance trajectory tracking accuracy and driving stability,a fuzzy model predictive controller (FMPC) is designed based on the equation of state for vehicle,the fuzzy control principle and the model predictive control principle.A simulation model is established to verify the effectiveness of the proposed method.The simulated results show that the proposed method can be used to avoid the multiple random static and dynamic obstacles,and the reference trajectory can be quickly and smoothly tracked after obstacles avoidance.Based on the analysis of obstacles avoidance stability,it is concluded that the target speed is 100km/h,the maximum lateral speed is 4.01km/h,the maximum yaw rate is 20.8°/s,and the maximum centroid side slip angle is 2.32°,which meet the requirements of driving stability.The proposed method effectively improves the obstacle avoidance ability and driving stability of intelligent vehicles.

In view of the time limit and complexity of ammunition support operation of carrier-borne aircraft, a scheduling optimization method is proposed for the ammunition support operation of carrier-borne aircraft facing the elevator hatch through scene modeling and task allocation under the premise of the known support task. A transportation path model with obstacle avoidance principle, an ammunition distribution model with equilibrium principle, and a carrier-based aircraft ammunition transport scheduling model with both actual combat and high efficiency are constructed. Based on the actual operation situation, the guarantee object of each elevator hatch is determined by Safe A^* algorithm and greedy algorithm, and an improved genetic algorithm based on chromosome segment coding is proposed to solve the scheduling model with the goal of minimizing the completion time of ammunition support operation. The results show that the proposed method is better than other allocation strategies and scheduling algorithms in terms of time consuming and resource utilization, which verifies its feasibility and efficiency in the actual ammunition support process.

In the context of large-scale unmanned aerial vehicle (UAV) swarm cooperative flight scenarios, the high computational time consumption in swarm path planning is caused by frequent path conflicts. Aiming at the problem above,a large-scale UAV swarm path planning method based on reinforcement learning conflict resolution is developed. A dual-layer planning architecture, comprising a high-level layer of conflict resolution and a low-level layer of path planning, is constructed to reduce the spatial and temporal dimensions of path conflicts. At the high-level layer of conflict resolution, a conflict resolution strategy network based on the Rainbow deep Q-networks (DQN) algorithm training framework is designed. This network transforms the resolution process of each path conflict into the action selection process of left and right tree nodes of a binary tree. This approach maps different conflict resolution sequences to their outcomes, thereby reducing the traversal of tree nodes and improving the efficiency of conflict resolution. At the low-level layer of path planning, the time dimension is incorporated into the spatial collision avoidance strategy. A re-planning jump point search (ReJPS) method based on a node re-expansion mechanism is proposed, which increases the feasible planning domain and enhances the ability to resolve the path conflicts. Simulated results indicate that, compared to the path planning methods based on the conflict-based search (CBS)+A^* and CBS+ReJPS, the proposed method reduces the average planning time by 86.64% and 19.65%, respectively, while maintaining comparable optimality.

At present, the determination of segmentation scale in the object-oriented seafloor acoustic image classification is empirical and significantly influenced by human factors. A spatially adaptive segmentation scale determination method using the confusion index as an objective index is proposed. The mean value and standard deviation of echo intensity corresponding to the segmentation objects are calculated by giving a set of segmentation scales. The unsupervised K-means clustering algorithm is then adopted to calculate the confusion indexes pf classification results at different segmentation scales, and the segmentation scale corresponding to the minimum confusion index is selected as the optimal scale to extract the seafloor image features. Based on the seafloor image features extracted at the optimal scale, a supervised classification model is established by combining the sampled data to predict the distribution of sediments in the whole surveying area. Experimental results prove that the spatially adaptive segmentation scales can be used to improve the classification accuracy significantly. The effectiveness of the proposed method is verified by cross-check in the experiment. Moreover, for thesegments that are with the relatively consistent the echo intensity characteristics, the classification accuracy can be further improved by introducing the terrain features.

The effective detection and tracking of water columns at marine impact points using visible light images is key to automatically check a target at sea. The existing detection and tracking algorithms still have a high false alarm rate and identity switch times (IDs) due to the movement of camera, the adjustment of focal length, and the changes of water columns. To solve the above problems, this paper proposes a detection and tracking algorithm based on dynamic features for water columns at marine impact points. The YOLOv8 target detector is used to detect the static water columns, and a small target detection head is added to the shallow feature map to enhance the model’s ability to detect small water columns. An improved ByteTrack tracker is used to track the water columns, and the tracking offsets caused by camera movement is compensated by combining camera movement and Kalman filtering. And then, a support vector machine is used for comprehensive decision-making to judge the water columns according to the spatiotemporal features of the water columns formation stage. Compared with traditional detection and tracking algorithms, the proposed algorithm is used to improve the three key performance indicators of multiple object tracking accuracy (MOTA), identification F1 (IDF1), and multiple object tracking precision (MOTP) by 7.8%, 5.1%, and 0.9%, respectively, the number of false positives (FP) is reduced by 112 times, and the numbers of IDs and false detections are both reduced to zero. Experimental results show that the proposed algorithm can not only accurately detect and track the water columns but also effectively exclude other interfering factors, thus achieving a significant enhancement in overall performance.

Autonomous underwater vehicle (AUV),as one of the important underwater detection tools,are widely used in various marine military operations.Most of the existing research on AUV obstacle avoidance and path planning focuses on grid maps,and rarely considers the real maneuverability of AUVs under water.In order to solve this problem,an improved proximal policy optimization based on positive-experience retraining (PR-PPO) algorithm and an AUV obstacle avoidance and path planning method based on artificial potential field are proposed.A dynamic artificial potential field is constructed by using the sensor in AUV model and the underwater environment in the simulation software.Based on the PR-PPO reinforcement learning algorithm,the mapping relationship between the AUV state and the action is established by interacting with the environment.Real-time obstacle avoidance and path planning can be realized without dynamic model and map information.The results show that,compared with the traditional D3QN and PPO algorithms,the proposed algorithm can not only ensure the success rate of the task,but also shorten the model training time and improve the convergence effect.