美军有/无人机协同作战研究现状与分析

近几年来,以"忠诚僚机"作战模式为主要代表的有/无人机协同作战被日益关注,并成为未来第六代穿透型空中作战的发展趋势之一。文章简介了有/无人机协同作战的内涵,并以美军为主要对象,分析其在有/无人机协同作战方面的主要进展,梳理有/无人机协同作战的关键技术。对我国相关技术和装备的发展,具有一定的参考价值。     

基于有限元-刚体混合模型的运载火箭缓释放特性分析

以一种新提出的新型强制式牵制释放系统为例,建立了分析运载火箭缓释放过程中缓释特性的有限元—刚体混合模型。采用刚体模型计算运载火箭的运动,利用有限元方法计算缓释销的弹塑性大变形接触过程,缓释机构和运载火箭的界面满足力和位移的协调条件。该混合模型采用增量方法进行求解,其可用来分析缓释机构的不同材料、几何形状、尺寸以及摩擦条件对运载火箭的完全释放时间、释放时刻速度以及缓释机构消耗功等参数的影响。     

自主水下航行器导航技术

水下导航技术是自主水下航行器发展的关键技术,因而得到了广泛的关注。简要介绍了自主水下航行器的重要性及国外的发展概况,对国外水下航行器所采用的导航技术进行了综述,并就我国应采取的对策与措施提出建议。     

高超声速滑翔式飞行器再入轨迹多目标多约束优化

轨迹优化是高超声速滑翔式飞行器关键技术之一.为避免间接法求解轨迹优化问题时对初值敏感以及伪谱法求解轨迹优化问题中难以处理航路点和禁飞区等问题,提出采用基于Akima插值多项式的直接法求解高超声速滑翔式飞行器再入轨迹优化问题.以驻点热流密度最小和到达目标点时间最小为优化目标,考虑了终端约束、航路点约束、禁飞区约束、动压约束、过载约束等约束条件.仿真结果表明:采用的插值方法可以减少插值过程中的控制变量越界问题;得到的Pareto前沿具有良好的分布性,在初步设计时能够给设计者较大的选择余地;和文献中的单目标优化结果相比,本文所采用的算法也具有较好的寻优能力.     

自旋稳定动能拦截器轨控发动机推力控制

提出了大气层外自旋稳定式动能拦截器的一种典型轨控发动机阵列布局.在分析小发动机工作的各种约束条件的基础上,推导了发动机推力角度、推力大小和冲量效率的计算公式,提出了发动机工作的控制方法.在各种初始条件下进行拦截仿真,所得的脱靶量均在0.3 m以内,可视为直接碰撞命中目标.同时通过仿真分析了冲量效率与自旋速度、发动机工作时间、每圈发动机个数的关系.     

MEMS惯性测量组合在无人机飞行参数测量中的应用

介绍了在无人机飞行试验中应用MEMS微惯性测量组合来解算飞行姿态、速度位置等参数的某个实例.试验结果表明,由此系统所得到的数据及曲线能够很好地描述实际飞行过程.试验数据为进一步地相关分析提供了有利的依据.     

坦克装甲车辆火控观瞄系统发展趋势分析

从火控总体的角度总结了国内外坦克装甲车辆火控观察瞄统的发展过程、现状,分析了观察瞄统的发展特点.对新近发展起来的顶置遥控武器站进行了初步研究,对其应配属的观瞄系统形式进行了探讨.在对未来装备需求和技术发展深入分析的基础上,提出了坦克装甲车辆火控观瞄系统未来的发展方向和需要重点突破和解决的关键技术.     

基于SINS/DVL/GPS的AUV组合导航技术

为了提高自主水下航行器组合导航系统的精度,选择了捷联式惯性导航系统(SINS)和多普勒速度声纳(DVL)导航为主、GPS卫星导航系统为辅的组合导航方法.通过Kalman滤波技术对组合导航的误差状态进行了估计,并采用反馈校正的方法修正SINS的导航误差.仿真结果表明SINS/DVL/GPS的组合导航可以有效地提高水下航行器SINS/DVL组合导航系统的导航精度,满足AUV远距离航行的精度需求.     

Research on simultaneous localization and mapping for AUV by an improved method: Variance reduction FastSLAM with simulated annealing

At present, simultaneous localization and mapping (SLAM) for an autonomous underwater vehicle (AUV) is a research hotspot. Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion, an improved method of variance reduction fast simultaneous localization and mapping (FastSLAM) with simulated annealing is proposed to solve the problems of particle degradation, particle depletion and particle loss in traditional FastSLAM, which lead to the reduction of AUV location estimation accuracy. The adaptive exponential fading factor is generated by the anneal function of simulated annealing algorithm to improve the effective particle number and replace resampling. By increasing the weight of small particles and decreasing the weight of large particles, the variance of particle weight can be reduced, the number of effective particles can be increased, and the accuracy of AUV location and feature location estimation can be improved to some extent by retaining more information carried by particles. The experimental results based on trial data show that the proposed simulated annealing variance reduction FastSLAM method avoids particle degradation, maintains the diversity of particles, weakened the degeneracy and improves the accuracy and stability of AUV navigation and localization system.     

Clustered coverage orienteering problem of unmanned surface vehicles for water sampling

This study investigates a clustered coverage orienteering problem (CCOP), which is a generalization of the classical orienteering problem. The problem is widely motivated by the emerging unmanned techniques (eg, unmanned surface vehicles and drones) applied to environmental monitoring. Specifically, the unmanned surface vehicles (USVs) are used to monitor reservoir water quality by collecting samples. In the CCOP, the water sampling sites (ie, the nodes) are grouped into clusters, and a minimum number of nodes must be visited in each cluster. With each node representing a certain coverage area of the water, the objective of the CCOP is to monitor as much as possible the total coverage area in one tour of the USV, considering that overlapping areas provide no additional information. An integer programming model is first formulated through a linearization procedure that captures the overlapping feature. A two-stage exact algorithm is proposed to obtain an optimal solution to the problem. The efficiency and effectiveness of the two-stage exact algorithm are demonstrated through experiments on randomly generated instances. The algorithm can effectively solve instances with up to 60 sampling sites.