SNR准则下的认知MIMO雷达波形设计

研究了MIMO雷达信号模型下基于最大化信噪比(SNR)准则的最优和稳健波形设计问题.根据对目标冲击响应功率谱密度(PSD)确定和不确定两种条件的分析,推导了最优波形和稳健波形的闭式表达式.仿真结果表明,当目标冲激响应PSD的先验知识准确时,使用最优波形作为发射波形,在接收端获得在给定条件下最优的SNR;当目标冲击响应PSD的先验知识不准确时,使用稳健波形作为发射波形,能够降低目标冲激响应PSD的不确定性对输出端信噪比(SNR)的负面影响.     

郭兴福教学法的回顾与思考

郭兴福教学法是在20世纪60年代初人民解放军军事训练由战争年代"以战教战"向和平时期"以训备战"战略转变及贯彻"以我为主"军事训练方针的时代背景下诞生的。它经历了萌芽培育、成形提高、推广普及三个阶段。郭兴福教学法科学地反映了军事训练的客观规律,既有鲜明的时代特色,又有长久的价值和强大的生命力。在新形势下,继承和发展郭兴福教学法,重在把握其精髓实质,着力解决运用过程中遇到的新情况、新问题。     

乱世元勋的为将为臣与为人——唐代名将郭子仪功业中的人格因素分析

封建专制时代的将领,建功立业不仅要有非凡的军事才能,还要有高超的人事协调艺术。否则,非但功业难成,身家性命也难保全。本文以唐代名将郭子仪为蓝本,通过对郭子仪为臣、为将、为人三方面处世之道的深入分析,廓清了封建专制时代武将建功立业不得不具备的人格因素。     

Bin‐packing problem with concave costs of bin utilization

We consider a generalized one‐dimensional bin‐packing model where the cost of a bin is a nondecreasing concave function of the utilization of the bin. Four popular heuristics from the literature of the classical bin‐packing problem are studied: First Fit (FF), Best Fit (BF), First Fit Decreasing (FFD), and Best Fit Decreasing (BFD). We analyze their worst‐case performances when they are applied to our model. The absolute worst‐case performance ratio of FF and BF is shown to be exactly 2, and that of FFD and BFD is shown to be exactly 1.5. Computational experiments are also conducted to test the performance of these heuristics. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

分布式网络化测试系统设计

以某大型复杂装备远程测试系统为例,给出了基于DCOM的分布式网络化虚拟仪器测试系统的体系结构.该网络化测试系统体系结构采用DS技术作为客户端和测试端连接的中间件,并采用C/S、B/S混合模式使系统各COM/DCOM组件通过网络实现"无缝"连接.DCOM技术的应用使得各网络测试节点成为服务的本体,可以响应不同客户的测试需求,实现了良好的数据传输、安全管理和远程仪器控制.DS技术的使用进一步简化了计算机和测试应用之间的数据交换,达到了良好的实时传输效果.     

An efficient SPH methodology for modelling mechanical characteristics of particulate composites

Particulate composites are one of the widely used materials in producing numerous state-of-the-art components in biomedical, automobile, aerospace including defence technology. Variety of modelling techniques have been adopted in the past to model mechanical behaviour of particulate composites. Due to their favourable properties, particle-based methods provide a convenient platform to model failure or fracture of these composites. Smooth particle hydrodynamics (SPH) is one of such methods which demonstrate excellent potential for modelling failure or fracture of particulate composites in a Lagrangian setting. One of the major challenges in using SPH method for modelling composite materials depends on accurate and efficient way to treat interface and boundary conditions. In this paper, a master-slave method based multi-freedom constraints is proposed to impose essential boundary conditions and interfacial displacement constraints in modelling mechanical behaviour of composite materials using SPH method. The proposed methodology enforces the above constraints more accurately and requires only smaller condition number for system stiffness matrix than the procedures based on typical penalty function approach. A minimum cut-off value-based error criteria is employed to improve the compu-tational efficiency of the proposed methodology. In addition, the proposed method is further enhanced by adopting a modified numerical interpolation scheme along the boundary to increase the accuracy and computational efficiency. The numerical examples demonstrate that the proposed master-slave approach yields better accuracy in enforcing displacement constraints and requires approximately the same computational time as that of penalty method.     

Research on the influences of confining pressure and strain rate on NEPE propellant: Experimental assessment and constitutive model

In order to study the influences of confining pressure and strain rate on the mechanical properties of the Nitrate Ester Plasticized Polyether (NEPE) propellant, uniaxial tensile tests were conducted using the self-made confining pressure system and material testing machine. The stress-strain responses of the NEPE propellant under different confining pressure conditions and strain rates were obtained and analyzed. The results show that confining pressure and strain rate have a remarkably influence on the mechanical responses of the NEPE propellant. As confining pressure increases (from 0 to 5.4 MPa), the maximum tensile stress and ultimate strain increase gradually. With the coupled effects of confining pressure and strain rate, the value of the maximum tensile stress and ultimate strain at 5.4 MPa and 0.0667 s⁻¹ is 2.03 times and 2.19 times of their values under 0 MPa and 0.00333 s⁻¹, respectively. Afterwards, the influence mechanism of confining pressure on the NEPE propellant was analyzed. Finally, based on the viscoelastic theory and continuous damage theory, a nonlinear constitutive model considering confining pressure and strain rate was developed. The damage was considered to be rate-dependent and pressure-dependent. The constitutive model was validated by comparing experimental data with predictions of the constitutive model. The whole maximum stress errors of the model predictions are lower than 4% and the corresponding strain errors are lower than 7%. The results show that confining pressure can suppress the damage initiation and evolution of the NEPE propellant and the nonlinear constitutive model can describe the mechanical responses of the NEPE propellant under various confining pressure conditions and strain rates. This research can lay a theoretical foundation for analyzing the structural integrity of propellant grain accurately under working pressure loading.     

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capa-bility for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of task-decomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV's control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV's flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.     

Adaptive control of track tension estimation using radial basis function neural network

Track tension is a major factor influencing the reliability of a track. In order to reduce the risk of track peel-off, it is necessary to keep track tension constant. However, it is difficult to measure the dynamic tension during off-road operation. Based on the analysis of the relation and external forces depending on free body diagrams of the idler, idler arm, road wheel and road arm, a theoretical estimation model of track tension is built. Comparing estimation results with multibody dynamics simulation results, the rationality of track tension monitor is validated. By the aid of this monitor, a track tension control system is designed, which includes a self-tuning proportional-integral-derivative (PID) controller based on radial basis function neural network, an electro-hydraulic servo system and an idler arm. The tightness of track can be adjusted by turning the idler arm. Simulation results of the vehicle starting process indicate that the controller can reach different expected tensions quickly and accurately. Compared with a traditional PID controller, the proposed controller has a stronger anti-disturbance ability by amending control pa-rameters online.     

Recent progress on transition metal oxides and carbon-supported transition metal oxides as catalysts for thermal decomposition of ammonium perchlorate

As a main oxidizer in solid composite propellants, ammonium perchlorate (AP) plays an important role because its thermal decomposition behavior has a direct influence on the characteristic of solid com-posite propellants. To improve the performance of solid composite propellant, it is necessary to take measures to modify the thermal decomposition behavior of AP. In recent years, transition metal oxides and carbon-supported transition metal oxides have drawn considerable attention due to their extraor-dinary catalytic activity. In this review, we highlight strategies to enhance the thermal decomposition of AP by tuning morphology, varying the types of metal ion, and coupling with carbon analogue. The enhanced catalytic performance can be ascribed to synergistic effect, increased surface area, more exposed active sites, and accelerated electron transportation and so on. The mechanism of AP decom-position mixed with catalyst has also been briefly summarized. Finally, a conclusive outlook and possible research directions are suggested to address challenges such as lacking practical application in actual formulation of solid composite propellant and batch manufacturing.