An air cavity method for increasing the underwater acoustic targets strength of corner reflector

In order to improve the underwater acoustic target strength of corner reflectors, according to the principle of acoustic impedance mismatch of the boundary layer, the method of using air cavity to increase the underwater acoustic target strength of corner reflectors is proposed. The acoustic reflection coefficients of underwater air layer and single layer metal sheet are calculated and compared. The results show that the reflection coefficient of single layer metal sheet is greatly affected by frequency and incidence angle, and the reflection coefficient of air layer in water is large and little affected by frequency and incidence angle. On this basis, a new kind of airfilled cavity corner reflector is designed. The acoustic scattering characteristics of underwater airfilled cavity corner reflector are calculated cumulatively, and the results are compared with the monolayer metal sheet corner reflector. The simulation results show that the acoustic reflection effect of the airfilled cavity corner reflector is better. In order to verify the correctness of the method, the test was carried out in the silencing tank. The experimental results show that the simulation results are in good agreement with the experimental results, and the airfilled cavity can improve on acoustic reflection performance of the underwater corner reflector.     

防空兵群射击指挥方式及其优化问题

从防空兵作战指挥的实际需求出发,利用概率论和防空作战运筹理论,描述了集中、分散、混合和平行等射击指挥方式的概念、特点及其相互关系,论述了射击指挥方式定量分析的基本方法、主要依据和指标,建立了基于群抗击效率的评估模型,在对多种射击指挥方式进行定量比较的基础上,揭示了射击指挥中具有普遍意义的、潜在的实用规则.     

反舰导弹对目标信息迟滞的适应性分析

目标信息迟滞(延误)情况下的导弹攻击是导弹超视距攻击的一种常态。以直升机引导反舰导弹实施超视距攻击为例,在全面分析反舰导弹超视距攻击射击误差因素的基础上,建立了反舰导弹捕捉概率模型,对目标信息迟滞对导弹捕捉概率的影响进行了精确定量分析,得出了相关结论,并对导弹超视距攻击的引导与实施提出了建议与要求。     

基于模糊推理的雷达辐射源识别方法

辐射源数据往往不完整或不确定,因此应用模糊推理实现了辐射源和目标平台的识别.将辐射源特征转换为由模糊集及隶属度函数表征的参数,再通过与数据库中的辐射源数据进行模糊匹配来判断辐射源的类型,实现了雷达辐射源的被动识别方法.进一步通过识别出目标平台上所装备的雷达辐射源的参数值,利用模糊推理,推断出目标平台的类型.试验结果表明:模糊推理的算法是切实可行的,在对模拟数据加入的噪声不超过一定限制的情况下,对于数据库已有的目标能够达到较高的目标识别率.     

An infrared target intrusion detection method based on feature fusion and enhancement

Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning. In view of the characteristics of intrusion targets as well as inspection difficulties, an infrared target intrusion detection algorithm based on feature fusion and enhancement was proposed. This algorithm combines static target mode analysis and dynamic multi-frame correlation detection to extract infrared target features at different levels. Among them, LBP texture analysis can be used to effectively identify the posterior feature patterns which have been contained in the target library, while motion frame difference method can detect the moving regions of the image, improve the integrity of target regions such as camouflage, sheltering and deformation. In order to integrate the advantages of the two methods, the enhanced convolutional neural network was designed and the feature images obtained by the two methods were fused and enhanced. The enhancement module of the network strengthened and screened the targets, and realized the background suppression of infrared images. Based on the experiments, the effect of the proposed method and the comparison method on the background suppression and detection performance was evaluated, and the results showed that the SCRG and BSF values of the method in this paper had a better performance in multiple data sets, and it’s detection performance was far better than the comparison algorithm. The experiment results indicated that, compared with traditional infrared target detection methods, the proposed method could detect the infrared invasion target more accurately, and suppress the background noise more effectively.     

空间非合作目标捕获方法综述

随着空间事故和失效航天器的增多,地球附近的轨道资源越来越紧张。如今世界各国都在大力发展在轨服务和太空碎片清除,作为其中最关键的技术之一,空间非合作目标捕获近年来成了太空研究领域的重点。目前,国内外各大研究机构针对非合作目标进行了深入研究,并提出了多种捕获方法,取得了较大的进展。对非合作目标进行分类,基于现有的捕获方法介绍了国内外非合作目标捕获技术发展的最新状况,总结了原理方案,分析了捕获方法和关键技术,为我国后续开展相关研究提供了一种研究思路。     

基于IMM-RUASFF的网络化目标跟踪算法

针对目前网络化目标跟踪算法存在实时性差、精度低等问题进行了研究。首先,基于网络信息共享需求,建立了网络探测节点的目标跟踪模型;其次,网络探测节点目标跟踪需求和实战要求发现目标经常是有多种运动状态并存,而单一模型的滤波器不能满足对机动目标跟踪性能的要求,采用了基于交互式多模型(Interacting Multiple Model,IMM)的有反馈实时更新的异步状态融合算法。最后,针对多个探测节点目标跟踪的状态融合估计问题,提出了一种有反馈实时更新的异步状态融合算法,通过仿真验证了算法的有效性。     

基于多元统计的空中目标区分方法

现代战争中的空情信息复杂多变,如何对空中目标合理地区分,以提取其主要信息,是空情信息处理的一个重要方面。基于多元统计的思想,采用聚类分析和判别分析的方法,对空中目标进行区分、辨别,以此为指挥人员提供作战决策的依据。     

舰载直升机在舰艇编队对低空目标预警中的使用研究

结合水面舰艇编队对低空目标预警目前存在的问题 ,指出了使用舰载直升机对低空目标预警的必要性和可行性 ,论述了舰载直升机用于编队低空预警的优点以及配置方法 ,最后指出了舰载直升机用于低空预警时的几点注意事项     

基于门限的无线传感器网络密钥管理方案

由于无线传感器网络在能量消耗、内存开销和计算能力等方面的局限性，传统的网络密钥管理方法已不适用。为此提出了一种适用于无线传感器网络的密钥预分配方法——基于（t，n）-门限方案，给出了密钥分配方法，并从方案的连通性、安全性等方面进行了有效的分析，结果表明该算法在这些方面有一定的优势。