防区外多导弹协同突防复合制导研究

针对防区外多导弹协同突防要求导弹能够成功突破敌防御系统并击中目标的问题,提出了一种基于虚拟目标的复合制导规律。首先,在敌雷达探测范围的边缘设置虚拟目标,将导弹制导过程分为制导段Ⅰ和制导段Ⅱ。然后,研究了一种基于攻击时间和攻击角度控制的制导规律,使导弹在制导段Ⅰ按指定时间和角度追踪到虚拟目标,导弹转入制导段Ⅱ后在以攻击时间最短为指标函数的最优制导规律导引下攻击真实目标。仿真结果表明该复合制导规律大大提高了多导弹的整体突防能力。     

基于粗集理论证据加权的电子目标识别法

在对电子目标进行识别时,往往采取多传感器融合的D2S证据理论进行处理,但是由于传统的D2S证据理论中各传感器对识别结果的重要性没有区分,基于此将粗集理论属性重要度概念应用到各传感器的重要性上,从而实现加权融合的证据理论。仿真实验及其结果表明该方法对电子目标识别是有效的,尤其在传感器受到干扰时,具有较强的现实意义。     

炮兵营射击的弹着点模拟及射击误差分析

弹着点模拟是对敌火力打击目标毁伤仿真的重要环节。在分析炮兵营射击误差构成与计算方法的基础上,研究建立了炮兵射击的弹着点散布蒙特卡洛(Monte-Carlo)仿真模型,实现了对集火射向、适宽射向、三距离射击等多种火力打击方式共同作用下的弹着点散布模拟,为目标毁伤仿真研究提供了模型支持。以此为基础,通过分析各组误差对毁伤概率的影响,发现对于炮兵营打击面散布目标来说,营共同误差是影响毁伤概率的最显著因素,应当通过提高测定目标位置和气象准备的精度减小误差以提高对目标的毁伤效果。     

远程迷惑式干扰效果评估模型

在分析远程迷惑式干扰机理的基础上,研究迷惑式干扰的各种影响因素,分别引入了箔条云相似因子、时间有效性因子、编队模拟逼真度因子、雷达识别影响因子和战术选择影响因子等多个效果评估指标,提出了多箔条云数目的整合模型,然后求导出在远程迷惑式干扰务件下搜索雷达对真实目标的捕捉概率计算模型,最后验证了模型的合理性.     

脉冲末修弹落点预测导引方法研究

在分析剩余射程影响因素的基础上,以弹道特征参数弹道高y及速度矢量分量Vx和Vy为解释变量,以回归分析方法为数学工具,建立脉冲末修弹剩余射程预测模型。以目标点与弹丸预测落点之间的差值为广义弹道偏差,结合数值仿真,对落点预测导引进行深入研究,验证了回归分析模型的可行性。     

中远程空空导弹目标截获概率仿真计算

分析了影响中远程复合制导空空导弹截获目标的主要因素,建立了捷联惯导误差模型、载机雷达观测误差模型、雷达导引头误差模型等,提出了用蒙特卡洛法的方法计算截获概率,并给出仿真实例.仿真结果表明了模型和算法的有效性和实用性,为分析捷联惯导、载机雷达等对目标截获概率的影响提供了方法和支持.     

概率分布模型的飞机战备完好率评估与预测

飞机战备完好率是军用飞机战斗力的重要体现.在分析目前部队常用的飞机战备完好率算法缺陷的基础上,建立了基于概率分布的飞机战备完好率计算模型,该模型体现了飞机任务可靠性、维修性与战备完好性之间的关系.以某型飞机为例,通过分析飞机任务可靠度,利用飞机可靠性、维修性、保障性及作战训练任务的历史统计数据,依据概率分布模型对飞机的...     

液压水锤效应引起液体喷溅特性及其影响因素试验研究

为研究液压水锤效应引起的液体喷溅特性及其影响因素,进行了高速破片撞击充液容器的试验,测试了液体内的空腔振荡特性、压力分布特性和容器外的液体喷溅特性。试验结果表明:液压水锤效应引起的液体喷溅有两个不同的阶段。第一个阶段发生在空腔生长到最大体积后的400~700 μs内,喷溅液体的头部呈箭状;在第二个阶段出现多次形状相似的脉动喷溅,且单次脉动均发生在每次空腔溃灭之后,喷溅液体具有伞状头部与线状尾部。液体内的压力和侵彻孔的形态共同影响液体的喷溅速度,相对喷溅速度与破片的撞击速度成反比。喷溅液体在运动过程中存在速度波动,且随着喷溅次数的增加,液体喷溅速度逐渐下降,速度拐点出现的位置逐渐接近侵彻孔。     

Damage mechanics and energy absorption capabilities of naturalfiber reinforced elastomeric based bio composite for sacrificial structural applications

The present study deals with the experimental, finite element (FE) and analytical assessment of low ballistic impact response of proposedflexible'green' composite make use of naturally available jute and rubber as the constituents of the composite with stacking sequences namely jute/rubber/jute (JRJ), jute/rubber/rubber/jute (JRRJ) and jute/rubber/jute/rubber/jute (JRJRJ). Ballistic impact tests were carried out by firing a conical projectile using a gas gun apparatus at lower range of ballistic impact regime. The ballistic impact response of the proposed flexible composites are assesses based on energy absorption and damage mechanism. Results revealed that inclusion of natural rubber aids in better energy ab-sorption and mitigating the failure of the proposed composite. Among the three different stacking se-quences of flexible composites considered, JRJRJ provides better ballistic performance compared to its counterparts. The damage study reveals that the main mechanism of failure involved in flexible com-posites is matrix tearing as opposed to matrix cracking in stiff composites indicating that the proposedflexible composites are free from catastrophic failure. Results obtained from experimental, FE and analytical approach pertaining to energy absorption and damage mechanism agree well with each other. The proposed flexible composites due to their exhibited energy absorption capabilities and damage mechanism are best suited as claddings for structural application subjected to impact with an aim of protecting the main structural component from being failed catastrophically.     

Visual SLAM in dynamic environments based on object detection

A great number of visual simultaneous localization and mapping (VSLAM) systems need to assume static features in the environment. However, moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption. To cope with this challenging topic, a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed. To reduce the influence of dynamic content, we incorporate the deep-learning-based object detection method in the visual odometry, then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system. Experiment with both on the TUM and KITTI benchmark dataset, as well as in a real-world environment, the results clarify that our method can significantly reduce the tracking error or drift, enhance the robustness, accuracy and stability of the VSLAM system in dynamic scenes.