Abstract:The way to build a strong army is to win talents. The military capabilities are the key factors of military talents in future wars. From the perspective of position requirements on battlefield, in order to strengthen intelligence and win the future, cadets should to reinforced and trained in their capabilities for learning, thinking, innovation, and technology and science. In order to temper the skills and being competence at an operation post, the cadets also should be strengthened and fostered in their capabilities for command, operating, and coordination. Meanwhile, for the sake of strengthening physical power and adapting to the battlefields, the cadets should be enhanced and cultivated in their capabilities of athletics and psychology. Military academies being the main sources of military talent cultivation, the connotations and essence of the nine military capabilities should be deeply understand, closely surrounding the factors and conditions of talents capability generation, accurately grasping the differences between the relationships, the dialectical relationship between knowledge and capability, the integral relationship between in class and out of class, the harmonious relationship between commonality and individuality of all kinds of cadets. Only by effectively implementing the cultivation of cadets military capabilities into the whole process of education can the academies make the trained talents become the top military personnel who are competent to fight and fight to win.

Abstract:The fragility defect of the existing prescribed performance control (PPC) is discussed. Based on this, the basic conception of nonfragile PPC is proposed. Firstly, the basic framework and key technologies of PPC are briefly summarized. Then, the possible control singular problem caused by actuator saturation and disturbance of the existing PPC is systematically analyzed, the fragility problem of the existing PPC is pointed out, and the main idea of the nonfragile PPC methodology and three basic problems (i.e.,error sensing,envelope adjustment and prescribed performance guarantee) that should be solved are given. Finally, based on the basic conception of non-fragile PPC, feasible implementation schemes of which are presented, and the superiority is verified via compared simulations. The relevant researching results are able to make up for the fragility defect of PPC and also are beneficial for opening up the new research field of non-fragile PPC.

Abstract:In view of beyond-visual-range air combat maneuver decision-making, an air combat decision-making model based on tactical maneuver combination is proposed. Firstly, the beyond-visual-range air combat maneuver is described as two typical forms with the parametric language being as a basic action of beyond-visual-range air combat. Secondly, the Hierarchical Task Network (HTN) model is introduced, and the air-to-air missile attack zone is utilized for describing the air combat situation to construct an air combat HTN model with parameters. Finally, taking maneuvering time, launchable distance, and launchable time as objective functions, a multi-objective optimization model is constructed, and the moth-flame algorithm is used to optimize and solve the HTN network parameters. The experiments show that the moth-flame algorithm can quickly solve the optimal parameters in the current situation, and the obtained air combat tactical maneuver sequence can achieve the tactical purpose. The algorithm model in this paper can provide pilots with auxiliary decision-making, and also provide new ideas for studying intelligent beyond-visual-range air combat.

Abstract:The boom aerial refueling actuation system is a key system to achieve boom aerial refueling.On the basis of sorting out the main factors influencing the development of boom aerial refueling actuation system，the composition and technical characteristics of the boom aerial refueling actuation system of the main tanker in service are compared and analyzed，and the key technologies of the boom aerial refueling actuation system are refined.Finally，the development of boom aerial refueling actuation system is discussed from the aspects of actuation technology，control mode and system architecture，combined with the current situation of related technologies in China.

Abstract:To address the problem that aircraft composite materials containing holes are prone to damage failure under tensile loading, in order to study the effect of penetrating circular holes on the safety of composite materials, tensile tests were designed for specimens without and with holes according to the actual carbon fiber composites used in a certain type of aircraft. The macroscopic tensile performance parameters were obtained, and the effects of penetrating round holes on the tensile performance were compared and analyzed. Based on the nonprobabilistic reliability analysis method, the reliability of tensile strength before and after the increase of training intensity was calculated and analyzed respectively by using the minimum infinite parametric index. And the safety analysis method of composite materials was summarized.The results show that the penetrating circular hole causes a significant decrease in the tensile strength of this composite, while it has little effect on the tensile stiffness. The rate of decrease in tensile strength is proportional to the hole diameter. The penetrating round holes causes a significant decrease in the tensile strength reliability of the composite, and the degree of decrease increases with the increase of the hole diameter. The reliability decreases further when the training strength is increased. The tensile strength reliability of the composites with all three pore diameters decreases to below 1, which seriously affects the safety of the composites.

Abstract:A jamming recognition method based on gray-level co-occurrence matrix is proposed for discriminating micro-motion jamming patterns including modulation and repeater micro-motion jamming from micro-motion scatter-wave jamming and pulse convolution micromotion jamming. Firstly, from the perspective of image domain, the received radar signal matrix is grayed. Then, the texture parameters are extracted by using the graylevel cooccurrence matrix to construct feature parame ters. Finally, KNearest Neighbor (KNN) classifier is adopted to test the jamming recognition rate. The simulation results show that the recognition method can effectively classify and recognize target echo without jamming and three kinds of micromotion jamming patterns under condition of noisy environment. When the signaltonoise ratio is -5 dB, the overall recognition rate is over 98%. When the signaltonoise ratio is -5 dB, the recognition rate of four kinds of signal is over 90% under different jammertosignal ratios, and the overall recognition rate of each kind of signal is over 98%. When the signal-to-noise ratio is 5 dB, the recognition rate of four kinds of signal is over 95% under different jammer-to-signal ratios, and the overall recognition rate of each kind of signal is over 99%.

Abstract:The high-squint synthetic aperture radar (SAR) echo signal is characterized by serious coupling between azimuth di-rection and range direction, large range migration. Imaging with conventional range Doppler (RD) algorithm causes problems such as azimuth defocus and space variation. In order to solve the problems in the imaging process of high-squint SAR and imaging quality and calculation cost, a Learnable Range Doppler (LRD) imaging method for high-squint SAR based on deep unfolded network is proposed. This method combines RD algorithm with deep learning by using RD imaging depth steps to build an RD imaging study network structure. Taking the echo data as network input is to learn the imaging process from echo data to SAR image. Firstly, the learnable parameters of imaging network are determined based on the analysis of the echo signal model of high-squint SAR. Secondly, the SAR imaging network is designed according to the imaging process. Finally, the network is trained through unsupervised training method and output the learning imaging result. The simulation results of point targets and real scene targets show that the proposed method can effectively suppress side lobes, improve imaging accuracy and calculation efficiency, and meet the requirements of high-squint SAR imaging.

Abstract:Aimed at the problem that the traditional INS-assisted pseudorange detection model is difficult to sense the position deviation lower than the inertial navigation drift error, an INS-assisted BDS pseudo-range rate-consistent deception signal detection method is proposed. The slow divergence speed information of the inertial navigation error is utilized for constructing the pseudorange rate after INS calibration, and obtaining the consistency test by the difference between the actual measured satellite pseudorange rate, and using the idea of fault detection to formulate a deception signal detection plan. The detection threshold is reached. The experimental results show that the pseudorange model compares with serious divergence over time, the proposed method is suitable for the detection of both position spoofing and speed spoofing, reducing the detection false alarm rate, and ensuring the detection sensitivity for a long time. High-precision inertial navigation system assistance can effectively improve the detection performance.

Abstract:The software-defined networks (SDN) not only enable the control plane to be coupled with the data plane, further can be also used to optimize the aviation swarm network architecture. In view of satisfying the demand of large-scale aviation swarm networking, a controller deployment optimization algorithm for large-scale aviation swarm networks (ASNs) is designed. The proposed algorithm transforms the deployment of multiple controllers into two phases, i.e., the swarm division and sub-swarm deployment. First, the swarm is divided into sub-swarms according to the load balance, and then the multiple-objectives optimization is performed in each sub-swarm based on the global optimum to obtain the Pareto frontier solutions. The simulation experiment evaluates the performance of the proposed algorithm in terms of the load balance index, average propagation delay and average disconnection probability of the entire network. The experimental result shows that compared with the existing algorithms, the proposed algorithm effectively enhances the performance of the entire network, and has lower time complexity. And the proposed algorithm is available for addressing the controller deployment issue in large-scale and dynamic ASNs.

Abstract:Channel estimation is the premise for receiver to compensate the channel in the SC-FDE system. Aimed at the problems that particle weight degradation exists in the PF algorithm of the classic estimation algorithm of the system in combination with the PSO algorithm, a timeC-varying channel estimation method for SC-FDE system based on particle swarm optimization and improved PF algorithm is proposed. On the basis of analyzing the SC-FDE system communication principle and the establishment of the channel estimation dynamic space model, the principle of particle filtering is analyzed, the idea of PSO algorithm is introduced, the random particle sequence is obtained through Logistic mapping, and the PSO algorithm is used to improve the particle distribution area. Use MATLAB software to compare the PSOC-PF algorithm with LS algorithm, EKF algorithm, and DFT algorithm. The simulation results show that compared with other traditional channel estimation algorithms, the PSOC-PF algorithm has lower BER and NMSE both in Gaussian noise channel and in nonC-Gaussian noise channel environments, and better performance in a slow timeC-varying channel environment.

Abstract:Military information systems are playing an increasingly important role in system operations at the information age, and comprehensive evaluation of performance is helpful to grasp the overall situation of system operation and maintenance. In view of the system operation and maintenance performance evaluation problem in consideration of time stability, first, a 6+20 twoC-level index system is built up. Secondly, a performance evaluation model in consideration of time stability is established. Based on comprehensive subjective and objective information, the optimal index weights are determined by adopting the Lagrange multiplier method, After that, the weight of the evaluation time is determined with the variance value as the optimization goal. And the system operation and maintenance performance evaluation results are generated on the basis of determining index and evaluation time weights. Finally, the proposed method is verified by simulation examples.

Abstract:The Fisher market equilibrium is a classic problem in economics, which can be formulated as a linear weight complementarity problem. The new search direction is obtained by adjusting the center direction offset to the feasible point to ensure feasibility, and then the linear search is used to find the maximum update parameter that satisfies the neighborhood conditions to design an algorithm to solve Fisher market equilibrium problems. The feasibility of the algorithm is analyzed, and the iterative complexity of the algorithm is proved. Numerical experimental results show that the algorithm is effective for solving Fisher market equilibrium problems.

Abstract:In view of role dynamic switching of heterogeneous UAV swarm in air combat tasks, a wolf inspired role matching task division method of UAV swarm in air combat is proposed to improve the dynamic task execution ability to deal with complex air combat environment. Firstly, three individual characteristics of wolves are obtained by analyzing the hunting behavior characteristics of wolves, and the role matching labor division model of wolves is given. And then, based on the similarity of behavior mechanism between UAV swarm and wolf group, the division mechanism of wolf group is mapped to UAV swarm, and a role matching task division model of UAV swarm is proposed. Finally, in view of the target allocation problems under different task division, the target allocation models of attack and guidance tasks are established respectively. The simulation results show that the proposed role matching task division method can effectively solve the problem of dynamic role switching in air combat and improve the overall air combat capability.

Abstract:Evaluating the autonomous capability of groundC-attack unmanned aerial vehicle (UAV) under the condition of combat mission is one of the key problems to be solved urgently in the combat use of UAV. According to the operational process and characteristics of groundC-attack UAV, five autonomous capability influencing factors closely fitting its operational characteristics are selected, including perceptual detection, planning and decisionC-making, operational execution, security management and learning evolution, to build an evaluation index system of autonomous capability of groundC-attack UAV for the whole mission process. An autonomous capability evaluation model is established based on Bayesian network, the prior probability of root node is determined by improved entropy weight method, and an example is simulated by Bayesian software Netica. Aiming at the autonomous capability of groundC-attack UAV before, during and after mission, three reasoning modes of causal reasoning, truncation analysis reasoning and influencing factor reasoning are used for simulation verification and reasoning analysis. According to the simulation verification results, the dynamic adjustment suggestions of autonomous capability in each stage are given.

Abstract:n view of security deployment of network slices, a network slice deployment strategy based on security awareness is proposed. In the network slice deployment phase, virtual network functions (VNF) are mapped first, and security constraints between VNF and physical nodes are defined from the perspective of security requirements. Secondly, the security feature matrix of physical nodes in the slicing deployment process is extracted, and the probability distribution of the security feature matrix is output by the policy network and sorted. Finally, the strategyC-based reinforcement learning is utilized for solving the mapping results of VNF. After the VNF mapping is complete, the Dijkstra algorithm is used to map virtual links to obtain the security deployment result of network slices. The simulation results show that the proposed strategy is superior to GRC and SVNEC-RL algorithms in terms of longC-term costC-benefit ratio, request acceptance rate, network resource utilization, bandwidth utilization and time complexity, and can meet the security requirements of network slicing during deployment.

Abstract:Being limited by factors such as nonC-cooperation and incomplete collected data, distributed sensing networks are difficult to achieve full coverage of the electromagnetic situation in the target area. Therefore, it is necessary to study a technology to reconstruct the complete electromagnetic situation in the target area according to the incomplete sensing data, and then master the electromagnetic situation. This paper proposes an electromagnetic map reconstruction algorithm based on compressed sensing and the split Bregman as a consequence. According to the compressed sensing, the algorithm proposes a filtered subdistrict orthogonal matching pursuit algorithm, and uses it to reconstruct reference signal receiving power data of the target area, and then uses the split Bregman to improve the accuracy of the data, eventually gets more accurate data. After that, the algorithm uses the data to draw a complete electromagnetic map. The simulation experiments show that the reconstructed electromagnetic map is more close to the actual situation. Moreover, with the numbers of the sensing nodes being rare, the root mean square error between the reconstructed data and the actual one is less than 2.5. The algorithm has important theoretical significance and application value.