空间信息支持高能激光武器应用研究*

依据空间信息支持现代作战的主要内容,介绍了五大类空间信息状况,分析了新概念武器高能激光的基本原理,并就其应用过程建立了UML模型。在此基础上,设计了空间信息支持高能激光武器应用系统的框架,并就高能激光武器对空间信息的需求进行了重点分析,而后以信息的转换与传递为主线,探讨了高能激光武器运用空间信息的流程。最后对该领域研究的趋势和发展前景做出了展望,并提出对其未来发展的要求。     

基于HLA的海战场信息对抗仿真系统设计

海战场信息对抗仿真系统为海军信息作战理论与战法研究、信息作战装备的作战效果模拟提供了一种新的途径。在对体系对抗仿真的体系结构分析的基础上,给出了海战场信息对抗仿真系统的设计思想,提出了系统的功能和总体框架,并对其中的一些关键技术进行了分析和探讨。     

多操纵面战斗机动态控制分配策略研究(英文)

现代先进飞机配置多个操纵面且又冗余,这有利于实现非常规飞行,提高飞机敏捷性,使飞机在机动和故障时有更多的选择,但是同时也导致了飞机控制设计复杂度的增加.为了达到更好的效果,就必须利用控制分配器来充分利用多操纵面的不同带宽.从频率域角度出发,研究了动态控制分配方法,并将其应用于某型先进战斗机的控制分配器设计中,利用鸭翼的频率特性抵消府舱过载,经仿真验证,采用动态控制分配策略时,可以有效地减小座舱过载,同时还可以减小配平阻力,取得了良好的控制效果.     

坦克嵌入式训练系统信息采集与处理技术

以坦克嵌入式训练为对象,分析了训练中信息采集与处理任务的功能及特点,并结合某新型坦克的技术特点,提出了信息采集与处理任务的需求,建立了分布式附加结构的信息采集处理模型。模型采用模块化和总线互联结构,由功能独立的主控单元和采集单元构成,保证了实车信息采集与处理的实时性和可靠性。     

基于信息流的武器装备体系效能模型

信息流是武器装备体系构建的重要依据,信息流过程是武器装备体系效能发挥与建模的基本框架.首先,分析了武器装备体系中信息流的种类,及包括的主要信息内容;其次,围绕信息流过程,将武器装备体系视为一个控制系统,研究了基于各装备效能的武器装备体系效能模型建模方法及相关评估指标的计算方法;最后,以一个算例说明了方法的有效性.     

空军地面雷达信息综合技术

空军地面雷达信息综合技术研究是以面向防空作战任务的战场信息共享支持为目标,借助先进的通信体系结构和技术体制,将各级雷达信息有机地集成起来,形成一体化的战场信息共享支撑体系,以提高我军联合指挥控制能力.     

军用信息系统能力评价方法研究*

信息能力直接影响信息优势能力和指挥决策能力,如何分析和评军用信息系统信息能力对作战的影响是一个重要的研究课题。在分析信息能力概念及其流程的基础上,详细研究了信息能力的三个度量指标:完备性、准确性和时效性的评估方法,给出评估信息能力的解析数学模型,研究结果对信息战和信息优势具有参考价值。     

基于云模型的导弹信息作战指挥效能评估

针对导弹信息作战指挥效能评估中既包含定量指标,又包含定性指标,而对这些指标进行科学合理地统一评价尚存在很多困难的问题,运用云模型对其进行了研究。分析了导弹信息作战指挥能力的指标体系,介绍了云模型的相关理论,并给出了运用云模型进行效能评估的详细算法,利用云发生器得到了最终的评估结果。最后给出了一个评估实例,结果证明该方法有效可行,能很好地解决效能评估中定性语言到定量值之间的转化问题。     

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics. Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing. Owing to the complexity of marine environment and the particularity of underwater acoustic channel, noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing. In order to solve the dilemma, we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), minimum mean square variance criterion (MMSVC) and least mean square adaptive filter (LMSAF). This noise reduction technique, named CEEMDAN-MMSVC-LMSAF, has three main advantages: (i) as an improved algorithm of empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN can better suppress mode mixing, and can avoid selecting the number of decomposition in variational mode decomposition (VMD); (ii) MMSVC can identify noisy intrinsic mode function (IMF), and can avoid selecting thresholds of different permutation entropies; (iii) for noise reduction of noisy IMFs, LMSAF overcomes the selection of decomposition number and basis function for wavelet noise reduction. Firstly, CEEMDAN decomposes the original signal into IMFs, which can be divided into noisy IMFs and real IMFs. Then, MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs. Finally, both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained. Compared with other noise reduction techniques, the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals, which has the better noise reduction effect and has practical application value. CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection, feature extraction, classification and recognition of underwater acoustic signals.     

Optimal control policies in service systems with limited information on the downstream stage

Existing models in multistage service systems assume full information on the state of downstream stages. In this paper, we investigate how much the lack of such information impacts jobs' waiting time in a two‐stage system with two types of jobs at the first stage. The goal is to find the optimal control policy for the server at the first stage to switch between type‐1 and type‐2 jobs, while minimizing the long‐run average number of jobs in the system. We identify control policies and corresponding conditions under which having no or partial information, the system can still capture the most benefit of having full information.