Morphing aircraft， which can change their aerodynamic configuration as needed to optimize aerodynamics and flight performance throughout the entire mission， enhancing overall performance and expanding mission profiles， is one of the important development directions for future aircraft. The overall benefits of morphing technology are a key prerequisite for the practical application of morphing technology. The significance on assessing the overall benefits of morphing aircraft is pointed out， and the research status of the overall benefits assessment of morphing aircraft since 2000 is introduced， including aircraft types， morphing methods， assessment methods， and assessment results. The overall benefits of morphing aircraft are sorted from the aspects of performance benefits and comprehensive benefits. The overall benefits assessment results show that the benefits brought by morphing technology are not as huge as expected. The use of morphing technology can increase the maximum single overall performance index by 21%， and most single overall performance indices are improved by no more than 10%. There are also cases where the overall performance benefit is less than 1%， or even no benefit when using morphing technology； the use of morphing technology can bring benefits to some overall performance， but it can also lead to a decrease in some overall performance， and increase production and maintenance costs， etc.； from the scheme demonstration， detailed design， to production and test flight， as the research goes deeper， the overall benefits assessed gradually decrease. Optimization methods to improve benefits are summarized， and their characteristics are analyzed. The existing problems in the overall benefit assessment are summarized， the future of morphing aircraft overall benefit assessment is prospected， and development suggestions are given.

The development of the Israeli Iron Dome air defense system is reviewed， and based on a brief introduction to the composition and functional characteristics of the system， an analysis of the Tamir missile's general scheme， aerodynamic characteristics， detection and guidance， and warhead design scheme is highlighted.By counting the operation of Iron Dome， the system effectiveness is analyzed and the interception capability of a battery for rocket barrage is given. On the basis of the development path of Iron Dome， the technical trend of foreign short-range air defense systems is summarized.

Camouflage & protection is one of the effective means for air-defense and anti-missile ground equipment against enemy air reconnaissance and precision strike.The development trend of camouflage & protection equipment of foreign ground weapons in recent years is traced. The new technology progress of foreign research institutions is also summarized， such as Sweden Saab Defense Group （SAAB）， Israel Fibrotex Technology Company （Fibrotex）， the United States i2K Defense Company （i2K defense）， Russia Rusbal Company， etc. Furthermore， the development trend of camouflage and disguise technology approaches of foreign ground equipment is analyzed， which could provide reference for the development of camouflage & protection technology of Chinese ground equipment， and is significantly benefit for improving the survivability and combat effectiveness of air-defense and anti-missile system in the environment of offense and defense confrontation.

The future war represented by high-precision guided weapons and unmanned systems has a high demand for satellite navigation signals. Based on the theory of satellite navigation spoofing interference， the concept of agile navigation warfare supported by low-orbit constellation is proposed. The definition and connotation of the concept are given， and its winning mechanism is further explained. By installing spoofing interference equipment， signal suppression and spoofing with more maneuverability， flexibility， and wider coverage can be achieved. Based on the concept of agile navigation warfare， specific combat scenarios are designed from the aspects of combat objectives， battlefield environment， combat forces， combat operations， combat effects， etc. With multi-means support in the sea， land， air and other combat domains， through adaptive control of spoofing signal power and dynamic adjustment of spoofing strategies， it ensures continuous and stable spoofing interference signal output， achieving regional signal suppression of blue threat targets and gradual covert inductive spoofing and deviation. This provides capability to support the red side to obtain the battlefield initiative and effectively carry out defense and attack missions. The key technologies that need tobe broken through in the implementation of agile navigation warfare and their feasibility are analyzed and explained.

Aiming at the problem that the existing efficiency prediction methods are difficult to reflect the actual effectiveness of anti-missile equipment system， a method of efficiency evaluation of anti-missile equipment system based on "data-driven + deep learning" is proposed. On the basis of a large number of experimental data extraction， disposal and analysis， we construct grey wolf optimization （GWO）-deep belief network（DBN） model to train the data， so as to obtain the nonlinear fitting of the anti-missile equipment system efficiency. We conduct a simulation experiment with an anti-missile system efficiency evaluation as an example， and the results show that the evaluation method is feasible and reliable. It can provide high reference value and significance for the demonstration and improvement of the anti-missile equipment system.

We investigate the impact of the initial separation velocity on the trajectory and aerodynamics of the missile and booster during cold separation. A numerical simulation method that couples unsteady computational fluid dynamics with a six degree-of-freedom solver is adopted. The paper analysis the separation processes between the missile and booster under various initial separation velocities. Simulation results reveal two distinct flow structures during the separation process： one characterized by recirculation and the other characterized by wake interaction. The paper analyzes the relationship between these typical flow structures and aerodynamics. A fitting curve tailored to the separation problem is established to predict the trajectory. In addition， the paper analyzes the pitching characteristics of the missile. The analysis in this paper provides a theoretical basis for the design of separation strategies. The fitting curve established in this paper provides a rapid prediction method for trajectory design.

When a hypersonic vehicle undergoes direct force control in the atmosphere， the interaction between the control jet and the incoming flow creates disturbance flow fields. Traditional Reynolds-averaged Navier-Stokes （RANS） models struggle to accurately predict these disturbances， leading to uncertainties. In order to deepen the understanding of the influence of supersonic side-jet inflow interference on the control system and its mechanism， a highly analytical numerical calculation method for the turbulent flow field based on the cone-pillar-skirt model is established. Comparing delayed detached eddy simulation （DDES） and RANS models under varying grid densities reveals significant discrepancies in the downstream jet wake disturbance region. DDES improves the reliability of lateral jet control results， especially under well-resolved turbulent grid conditions. Supported by simulations， the study enhances understanding of turbulent characteristics in supersonic lateral jet-induced disturbances. Dynamic DDES methods show improved pressure prediction compared to traditional RANS simulations， offering insights for enhancing the prediction accuracy of direct force control systems.

In order to assist commanders in making decisions efficiently， this paper makes researches on the grouping of operational targets in situation awareness. Aiming at the characteristics of numerous targets， different functions and complex interrelationships in the operational system of systems， the graph matching idea is used to solve the target group type identification problem. A graph-based target group representation method is proposed， where the target is abstracted as a node and the interrelation is abstracted as an edge， which depicts the targets and their mutual relationship. A graph matching network model is designed to identify the type of target group. The model can jointly learn the vector representation of the group to be identified and the group of known types. The type of target group can be identified by calculating the similarity between the two groups. Experimental results show that the proposed method can efficiently identify the type of target groups.

To meet the demand of future information warfare， it is of great significance to build a digital parallel battlefield system for analyzing the actual battlefield system. Therefore， based on the related technologies of parallel system and digital twin， the ACP method of artificial system， computational experiment and parallel execution is used in this study， and the construction idea of digital parallel battlefield system is proposed. The construction process of artificial battlefield is introduced in detail from the aspects of battlefield entity， service， twin data and twins. The parallel execution flow of actual battlefield and artificial battlefield is analyzed. The method proposed in this study can provide the basis for commanders to design the combat plans， make the combat plans， organize the combat cooperation， and command and control the battle situation more quickly and accurately.

Considering a large number of radar interference signals could be built within a short period on the battlefield. Traditional convolutional neural networks face issues due to their large scale. It’s a challenge to deploy jamming recognition system on small-scale equipment. This paper proposes an improved lightweight convolutional neural network to solve the problem by adopting adaptive kernel and batch normalization technology to improve recognition efficiency. By extracting time-frequency character to construct training and testing database for neural network training. Experiment shows that the network achieves over 96% identification accuracy for six kinds of interference signals under -8dB JNR. Compared with other networks， it has a superior accuracy efficiency ratio.

Troposcatter communication is one of the important wireless communication means of U.S. army and has played an important role in the past military operations. In order to meet the operational requirements under the strategy of great power competition， the U.S. army is purchasing and equipping the next generation troposcatter （NGT） communication equipment. Aiming at the NGT communication equipment of the U.S. army， this paper systematically combs the main performance and current development trend， summarizes the main characteristics and advantages of the NGT equipment compared with the active equipment， and studies the later development trend combined with the concept and technology development of the U.S. army. Finally， the paper sums up the successful experience of the U.S. army in the construction of the NGT communication equipment.

Flying ad-hoc networks （FANET） using directional transmissions face challenges during network initialization due to frequent topology changes and fluctuations in link quality. To address these issues， a sensing-assisted rapid neighbor discovery method is proposed. The main idea of this method is to use a sensing mechanism to obtain neighbor node information， which accelerates network efficiency. In addition， this paper describes the adoption of a new integrated sensing and communication waveform， known as multiple-input multiple-output （MIMO） orthogonal time-frequency space （OTFS），which combats the Doppler effect in fast-changing channels and improves link quality. For FANET scenarios， a multi-target detection technique based on MIMO-OTFS integrated sensing and communication waveforms is studied at the physical layer. This physical layer sensing scheme is then mapped to the upper layer network， and a sensing-assisted efficient neighbor discovery algorithm is designed. Additionally， a integrated sensing and communication mechanism for collaborative discovery of multi-point is proposed. This mechanism indirectly senses and discovers potential targets by interacting with sensing information and neighbor discovery tables among neighboring nodes. The proposed mechanism enhances the initial networking efficiency of FANET. Simulation results demonstrate that the proposed scheme outperforms pure communication protocols. This leads to a significant reduction in the initial networking time of FANET， an increase in target sensing accuracy， and an overall improvement in network performance.

The measurement of the close time is the key task of planning the time-sensitive target kill chain（TSTKC）. Ignoring the overlapping relationships among the steps of TSTKC， previous studies have adopted methods based on linear accumulation. This paper proposes a dynamic measurement method for the close time of TSTKC based on overlapping relationships additionally. Simulation results show that this method is more accurate， which is helpful for the command organization to capture more strike opportunities. The method also adapts to the changes of the window of vulnerability， which can provide a theoretical basis and technical support for the construction， modelling and effectiveness evaluation of TSTKC.

High-resolution inverse synthetic aperture radar （ISAR） images play a crucial role in target recognition. However， obtaining high-resolution ISAR images requires prolonged radar exposure， which does not meet the demands of radar resource scheduling. In this paper， we propose a recognition method that guides radar image feature fusion based on incidence angles. A portion of pulse accumulation imaging during the dwell time is selected to meet the temporal adjustments and allocations of the remaining radar functions. Considering the differences in modulation phenomena among images of different aircraft models and the modulation variations of the same model at different incident angles， a hybrid attention residual module and angle-guided attention module are designed to focus the network on critical areas of the image and correlate target features with target attitudes. A feature fusion module is employed to integrate image features for fusion recognition. Through practical data of three types of aircraft， we demonstrate that this method achieves higher recognition accuracy while satisfying radar resource scheduling requirements.

The research on target’s three-dimensional imaging is of significant meaning in feature extraction and recognition. Precession is the typical micromotion for cone-shaped space targets， such as warhead and satellites， which can be used for target feature extraction and identification. Inverse synthetic aperture radar （ISAR） imaging of a precession target， which is a kind of fast spinning target， is faced with migration through range cell when we use traditional imaging algorithms. Target’s micro-motion model is built and the theory of micro-motion imaging is analyzed. The relationship among radar’s line of sight， direction of target’s precession axes and imaging plane is derived. A new three-dimensional reconstruction method of scattering centers is proposed. With the analysis of multi-static ISAR image and the micro-motion model， the association of scattering centers in different station’s ISAR image is built. The target’s three-dimensional imaging is accomplished. Simulation experiments show the presented method is efficient in micro-motion target’s three-dimensional imaging.

The airborne synthetic aperture radar（SAR） system realizes the large azimuth width by scanning. The scanning angular velocity is restricted by azimuth resolution， squint angle and other parameters. It is a difficult problem to design and implement airborne wide-width azimuth scanning SAR with both scanning efficiency and azimuth resolution. In order to realize azimuth wide-swath scanning in the air route， a beam control method is proposed. By deriving the scanning angle change rule between different scene images， and designing the start scanning angle and end scanning angle of each scene image according to the angle change rule， the designed beam control method can meet the resolution requirements of the scanning angle velocity of each scene image and has a time-varying angular velocity with a high scanning efficiency. Overlapping between scenes to ensure coverage and the target scene is located in the middle of the scanning width in the course.

Traditional methods can extract target contour features from inverse synthetic aperture radar （ISAR） images. However， they have the problem of low accuracy. Therefore， this article proposes a target contour extraction method from ISAR images based on an improved Clean algorithm. Based on the random occurrence of noise， this article establishes an optimization function for scattering point extraction， and uses the iterative Clean method to extract as many weak scattering points as possible. A scattering point quality evaluation method based on connectivity analysis is introduced. This method is used in the post-processing of the Clean algorithm. It denoises the points extracted by the Clean algorithm and preserves the scattering points of the target. The scattering point set is processed with alpha-shape and wavelet filtering to get the target contour. The simulation and measured data processing results show that this method gives more accurate contour than existing methods. It also has better noise resistance.

Prediction of target detection performance is the basis for radar adaptive resource scheduling and refined information processing design. However， conventional statistical models are difficult to accurately describe the statistical distribution of the radar scattering cross section of stealth targets， resulting in inaccurate detection performance prediction. For this reason， this paper proposes a radar detection performance evaluation method based on a hybrid chi-square distribution model， which models and evaluates the detection probability under constant false alarm conditions. Based on the hybrid model， the target detection probability is modeled under the condition of constant false alarm using the folded line approximation and Kummer transform method； the accuracy of the model is examined by Monte Carlo simulation； the hybrid model is compared with the traditional statistical model from the perspective of detection probability by fixing the false alarm rate. The simulation results show that compared with the traditional Swerling model， the single-pulse detection performance of the hybrid cardinality model has a large difference in different azimuth ranges， which better reflects the real detection scenarios and provides theoretical support for the overall optimization design of the stealth target detection system.

By leveraging the correspondence between spares configuration schemes and equipment support performance， the BP（back propagation） neural network model is used to adjust the mapping relationship between spares configuration schemes and their corresponding satisfaction and utilization rates. This facilitates the evaluation of the rationality of spares configuration scheme design. Using three types of equipment with different structural compositions as examples， the corresponding neural network prediction models for the satisfaction and utilization rates of spares are designed and trained with data samples. This achieves fast and high-precision calculations of the satisfaction and utilization rates of spare parts， with both the mean error and mean square error being less than 0.05%， demonstrating the effectiveness of the proposed method.

The current simulation analysis method for oscillator phase noise is limited by the inherent models， and the resulting noise curve fails to adequately reflect the nonlinear characteristics of the components， thereby posing challenges in guiding practical designs. This paper proposes an approach based on nonlinear models combined with impedance network analysis. A nonlinear component model that is established to ensure the effectiveness of the simulation. Impedance network analysis is applied to derive circuit parameters affecting the system's phase noise. A simulation circuit incorporating the nonlinear model is designed， and the effectiveness of the proposed approach is validated using the advanced design system （ADS） simulation platform. Experimental results indicate that the proposed method aligns more closely with practical design compared to traditional methods. The presented noise optimization scheme is feasible and effective， offering valuable insights for improving phase noise metrics and enhancing overall system performance in practical applications.