数字化城市交通向导仿真的蚁群优化算法

以求解旅行商问题的蚁群算法为基础,充分考虑交通向导最佳路径的具体要求,对算法的选择机制、更新机制以及协调机制作进一步改进,引入自适应的转移策略,并融入节约法,以克服基本蚁群算法计算时间长、易出现停滞等缺陷。以湖北荆门地区车辆选择路径为研究对象,采用蚁群优化算法建立了车辆最佳路径的模型,并对其进行了仿真分析。仿真实验结果表明,优化算法比基本蚁群算法的路径更优,寻路时间更短。     

基于改进A-Star算法的隐身无人机快速突防航路规划

针对现代战争中隐身无人机（UAV）在高严密的组网雷达防御体系下的生存及突防问题,提出了基于改进A-Star算法的隐身无人机战区突防航路规划技术。首先对隐身无人机突防过程进行了分析建模,分别建立了隐身无人机的运动学模型、动态雷达散射截面特性和组网雷达探测概率的计算模型。然后针对传统算法在解决隐身突防问题时的不足,充分考虑所规划航路时的快速性和安全性要求,设计了改进A-Star算法。在算法中引入了多层变步长搜索策略和无人机的姿态角信息,结合秩K融合准则,通过每段航迹上隐身无人机被组网雷达系统的发现概率来判断新航迹点的可行性。仿真结果表明,改进A-Star算法能够在复杂的组网雷达系统下快速生成更优的战区突防航路,具有一定的应用价值。     

天翼1无人机电动化改造可行性分析

电动飞机正在国内外通用航空领域蓬勃发展,并影响着未来航空的发展方向。利用电推进技术对无人机进行电动化改造,能大大提高无人机的安全性、可靠性和维修性,降低使用成本。以天翼1无人机为电动化改造对象,提出了电动化改造方案,详细计算分析了电推进系统中电动机和锂电池组的主要参数,并从无人机的重量重心、性能以及电推进系统的安装方式等方面分析了改造的可行性。分析结果表明,天翼1无人机电动化改造基本可行;并提出了后续改造实施中需注意的问题。     

螺旋桨安装效应对无人机气动特性影响

为解决某型飞翼布局无人机(UAV)带动力构型风洞试验最大升阻比相对无动力状态大幅下降的问题,采用计算流体动力学(CFD)方法对无人机无动力与带动力构型进行了数值模拟,数值模拟结果分别与无动力以及带动力风洞试验数据吻合良好,在此基础上深入研究了螺旋桨安装效应对无人机气动特性的影响。结果表明:推力螺旋桨与机身之间气动干扰产生的低压区致使阻力增加,从而导致飞机最大升阻比相比无动力状态下降了30.7%。针对无人机在推力螺旋桨影响下出现的最大升阻比下降问题,采用增大螺旋桨与机身之间距离的方法可以有效地消除机身后部出现的低压区,减小了阻力,提升了无人机最大升阻比。桨毂拉长方案在8°和9°迎角下最大升阻比分别提升了17.3%和15.4%。     

Adaptive double chain quantum genetic algorithm for constrained optimization problems

Optimization problems are often highly constrained and evolutionary algorithms(EAs)are effective methods to tackle this kind of problems. To further improve search efficiency and convergence rate of EAs, this paper presents an adaptive double chain quantum genetic algorithm(ADCQGA) for solving constrained optimization problems. ADCQGA makes use of doubleindividuals to represent solutions that are classified as feasible and infeasible solutions. Fitness(or evaluation) functions are defined for both types of solutions. Based on the fitness function, three types of step evolution(SE) are defined and utilized for judging evolutionary individuals. An adaptive rotation is proposed and used to facilitate updating individuals in different solutions.To further improve the search capability and convergence rate, ADCQGA utilizes an adaptive evolution process(AEP), adaptive mutation and replacement techniques. ADCQGA was first tested on a widely used benchmark function to illustrate the relationship between initial parameter values and the convergence rate/search capability. Then the proposed ADCQGA is successfully applied to solve other twelve benchmark functions and five well-known constrained engineering design problems. Multi-aircraft cooperative target allocation problem is a typical constrained optimization problem and requires efficient methods to tackle. Finally, ADCQGA is successfully applied to solving the target allocation problem.     

基于改进人工蜂群算法的无人机实时航迹规划

为了适应现代战争战场环境的复杂性,针对所建立的突发威胁模型,提出了一种基于改进人工蜂群算法的无人机航迹规划方法,对无人机进行动态航迹寻优,并通过MATLAB进行了仿真验证。仿真结果表明,所设计的方法能够根据实时出现的威胁状况进行动态航迹规划、评价和选优,同时满足实时性要求。     

风—车—桥系统车辆风荷载突变效应风洞试验研究

侧向风作用下,车辆通过桥塔或双车交会时车辆所受风荷载会发生突变,这将影响行车的安全性和舒适性。基于自主研制的移动车辆模型风洞试验系统,针对轨道交通车辆和公路交通车辆,分别采用三车模型和单车模型测试了车辆通过桥塔和双车交会时车辆风荷载的突变过程,讨论了风速、车速、车辆所处轨道位置及车辆类型等因素对车辆气动力系数的影响。研究表明车辆运行于桥塔区域及双车交会时,车辆三分力系数均有不同程度的突变趋势;车辆运动引起的列车风使车辆阻力系数的突变程度减弱,使桥塔遮风效应的影响区域变长;公路交通车辆风荷载突变效应较轨道交通车辆的要明显;双车交会时,背风侧车辆风荷载急剧减小,迎风侧车辆三分力系数变化较平稳。     

运动阵列非重叠孔径扩展算法

现有线阵列被动合成阵列算法无法满足非连续信号和非均匀线阵列的处理需要,难以被直接用来提高运动线阵列对时域重叠脉冲信号的空间分辨能力.针对这一问题,本文提出一种能够同时满足对非连续信号和非均匀线阵列处理需求的运动阵列非重叠孔径扩展算法.该算法首先通过二维MUSIC算法估计运动阵列的“相位校正因子”,进而完成阵列孔径扩展实现对多个时域重叠信号的高分辨方向估计.通过仿真研究可知:小孔径运动阵列利用本算法在不牺牲方向估计精度的条件下,可以有效提高对多信号的分辨能力,且具有较好的低信噪比适应能力.     

机动时间常数未知目标的拦截方法研究

由于目标机动时间常数会对拦截导弹的性能产生重要的影响,且一般基于目标机动动态考虑的制导律都假设这一常数是已知的,在实际的拦截情形中并不合理.针对这两点,本文采用成型滤波器表示目标的随机机动策略,对原系统的状态进行扩展,并利用扩展后的系统构建多模型自适应估计器的成员滤波器,以实现对于目标机动时间常数的在线估计.仿真结果表...     

An improved multiple model GM-PHD filter for maneuvering target tracking

In this paper, an improved implementation of multiple model Gaussian mixture probability hypothesis density (MM-GM-PHD) filter is proposed. For maneuvering target tracking, based on joint distribution, the existing MM-GM-PHD filter is relatively complex. To simplify the filter, model conditioned distribution and model probability are used in the improved MM-GM-PHD filter. In the algorithm, every Gaussian components describing existing, birth and spawned targets are estimated by multiple model method. The final results of the Gaussian components are the fusion of multiple model estimations. The algorithm does not need to compute the joint PHD distribution and has a simpler computation procedure. Compared with single model GM-PHD, the algorithm gives more accurate estimation on the number and state of the targets. Compared with the existing MM-GM-PHD algorithm, it saves computation time by more than 30%. Moreover, it also outperforms the interacting multiple model joint probabilistic data association (IMMJPDA) filter in a relatively dense clutter environment.