基于最优化模型的太阳影子定位方法

针对利用太阳影子实现目标的定位问题,分析得到影响影子长度的参数,确立了影长与太阳高度角、目标物长度、经纬度等的函数关系,并结合最小二乘法思想,建立了以理论影长与实际影长之差的平方和最小为目标的优化模型。以直杆为例,运用粒子群优化算法快速求解得到目标的可能位置,并与实际地点进行了比较。     

Path planning for moving target tracking by fixed-wing UAV

For the automatic tracking of unknown moving targets on the ground, most of the commonly used methods involve circling above the target. With such a tracking mode, there is a moving laser spot on the target, which will bring trouble for cooperative manned helicopters. In this paper, we propose a new way of tracking, where an unmanned aerial vehicle (UAV) circles on one side of the tracked target. A circular path algorithm is developed for monitoring the relative position between the UAV and the target considering the real-time range and the bearing angle. This can determine the center of the new circular path if the predicted range between the UAV and the target does not meet the monitoring requirements. A transition path algorithm is presented for planning the transition path between circular paths that constrain the turning radius of the UAV. The transition path algorithm can generate waypoints that meet the flight ability. In this paper, we analyze the entire method and detail the scope of applications. We formulate an observation angle as an evaluation index. A series of simulations and evaluation index comparisons verify the effectiveness of the proposed algorithms.     

Path optimization for the resource‐constrained searcher

We formulate and solve a discrete‐time path‐optimization problem where a single searcher, operating in a discretized three‐dimensional airspace, looks for a moving target in a finite set of cells. The searcher is constrained by maximum limits on the consumption of one or more resources such as time, fuel, and risk along any path. We develop a specialized branch‐and‐bound algorithm for this problem that uses several network reduction procedures as well as a new bounding technique based on Lagrangian relaxation and network expansion. The resulting algorithm outperforms a state‐of‐the‐art algorithm for solving time‐constrained problems and also is the first algorithm to solve multi‐constrained problems. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

基于改进ACO算法的多UAV协同航路规划

针对无人机(Unmanned Aerial Vehicle,UAV)在执行任务过程中遇到的诸如敌方防空火力、地形障碍及恶略天气等各类威胁源,采用威胁源概率分布的方法进行威胁的量化处理,构建任务空间的威胁概率密度分布图,有效消除了威胁源的差异性。根据UAV在任务飞行过程中的性能约束与时、空协同约束,同时考虑任务过程中UAV的损毁概率最小、任务航程最短,构建了相应的综合任务航路代价最优化目标函数。结合传统蚁群优化算法(Ant Colony Optimization,ACO)在解决此类问题中的不足,给出了相应的改进策略,提出采用协同多种群ACO进化策略来实现多UAV在满足时、空协同约束下的协同航路规划。通过相应的仿真计算表明,改进后的ACO协同多种群进化策略算法更适用于多UAV协同任务航路规划问题,具有一定的实用性。从而为多UAV协同任务航路规划问题的求解提供了科学的决策依据。     

蚁群算法在无人机航路规划中的应用

蚂蚁算法是一种新的源于大自然生物界的仿生随机优化方法。吸收了昆虫中蚂蚁的行为特征,通过其内在的搜索机制,在一系列组合优化问题求解中取得了成效。将蚁群算法应用于无人机(UAV)航路规划,提出了一种适用于航路规划的优化方法,可以为在敌方防御区域内执行攻击任务的无人机规划设计出高效的飞行航路,保证无人机以最小的被发现概率及可接受航程到达目标点,提高了无人机作战任务的成功率。仿真结果初步表明该方法是一种有效的航路规划方法。     

Decomposition algorithms for finding the shortest path between a source node and a sink node of a network

Decomposition algorithms for finding a shortest path between a source node and a sink node of an arbitrary distance network are developed. Different decomposition algorithms are proposed for different network topologies. Since Shier's algorithm compares very favorably with other decomposition algorithms in all the network topologies, we compare our algorithms against Shier's algorithm. It is shown that the efficiency of the proposed algorithms compares very favorably with Shier's algorithm. For special types of networks the computational requirements of the proposed algorithm is a polynomial of O(n²).     

直达波和杂波干扰下目标阴影逆合成孔径成像

对直达波和杂波条件下的阴影逆合成孔径雷达(SISAR)目标成像问题进行了研究。分析了直达波和杂波对侧影像畸变和运动补偿的影响,提出了一种新的直达波和杂波干扰下目标SI-SAR成像方法,即将回波信号与参考信号进行模拟对消,并利用目标回波的频谱特性剔除残留直达波,再对目标运动参数进行粗估计,采用粗搜索和精搜索2步实施准确的运动补偿,最终获得失真度较小的目标侧影像。仿真实验验证了算法的有效性。     

机载反辐射导弹和常规反舰导弹协同规划算法

机载反辐射导弹在攻击移动目标时容易受雷达关机影响而丢失目标,和常规主动雷达制导的反舰导弹协同攻击可以有效对抗目标雷达关机。为了达到协同作战目的,载机起飞前已知目标信息情况下,通过计划协同算法确定协同作战方案,方案包括载机和导弹各个航路点位置和到达该位置的时间点,然后按照在机场起飞前就制定好的方案实施具体作战行动。对计划协同算法进行了实例仿真,结果表明算法切实有效。     

SAR图像目标分割与方位角估计

图像分割和目标方位角估计是进行SAR (SyntheticApertureRadar)图像自动目标识别的重要步骤。文章提出了一种基于MRF (MarkovRandomfield)模型的SAR图像分割算法 ,利用ICM (IterativeConditionalMode)局部优化方法 ,获得MAP (maximumaposteriori)准则下的图像分割结果 ,将图像分割为目标、阴影、背景三部分。然后确定目标离雷达最近的点 ,从而得到目标的主导边界 ,并估计出目标的方位角。用MSTAR (MovingandStationaryTargetAcquisitionandRecognition)数据进行实验 ,估计方位角的准确性与现有算法的结果相比 ,具有明显提高     

基于量子双向RRT算法的多平台反舰导弹协同航路规划

针对反舰导弹航路规划面临的动态威胁环境和多平台协同打击问题,提出了一种基于量子双向RRT算法的反舰导弹协同航路规划方法。采用动态坐标设置动态威胁,实时地避开动态威胁;通过取预规划终点方法,实现对目标的时间和空间协同打击;结合量子进化思想,将RRT算法中的扩展方向量子化表示,提出了一种量子双向RRT算法,并应用于航路规划。仿真结果表明,该方法可有效规避动态威胁和解决多平台反舰导弹航路规划的协同问题,并显著地改善了RRT算法的全局收敛性,得到了航程更短的航路。     

基于CPSO算法的海空跨域无人航行器集群协同作业路径规划

针对海空跨域无人航行器集群在复杂水域环境下协同作业以追踪水下目标的任务,提出一种基于协同粒子群(CPSO)的协同作业路径规划算法.考虑不同无人航行器集群特性优势,合理分解并分配远距离追踪水下目标任务过程,并利用CPSO算法进行路径规划.在CPSO算法中,首先为无人机(UAVs)集群规划飞行路径,UAV飞行过程中探测水面...     

具有可控细节层次的阴影空间新算法

首先阐述了阴影空间算法 ,然后对基于层次结构化包围盒和光线投射技术的阴影空间新算法进行了详细地研究。采用本算法可使生成的阴影效果具有可控细节层次。     

绕月轨道阴影区域建模与轨道优化

针对绕月运行轨道建立月球阴影区域与地球阴影区域的数学模型,给出了卫星是否处于阴影区域的判据;利用黄金分割法搜索出了阴影区域的边界值。将禁忌搜索算法嵌入遗传算法中并结合具体案例,搜索出了阴影时长最短的最优目标轨道。所给出的绕月轨道阴影区域模型简单且能够满足工程精度需求,可用于星上自主预报算法;所给出的嵌入式遗传算法能有效避免陷入局部最优并快速收敛。     

多架无人机协同作战的路径规划

提出了一种协调多架无人机的编队同时到达目标的航路规划方法.首先根据敌方防御区域内雷达、导弹等威胁阵地的具体分布情况,采用划分Voronoi多边形的方法制定初始航路,然后通过叠加定长线段到初始航路上对初始航路进行离散化,最后采用动态链类比法调整航路并对航路进行光顺优化处理,使航路安全可飞.并用数字仿真技术对该方法进行了验证,结果表明该方法是可行的.     

基于电磁涡旋的雷达目标成像

作为信息载体的电磁波除了传统的携带信息方式外,近年来其波前以电磁涡旋形式展现的信息调制能力也越来越受到关注。本文综述了电磁涡旋在信息调制等方面的研究进展,阐述了其在雷达信息获取方面的潜在应用价值。针对圆形相控阵列,建立了电磁涡旋波照射下理想点散射目标的回波模型。将各阵元的接收信号按照与发射时相同的模式移相后,沿圆周积分即可获得雷达阵列的输出回波。该回波可表示为经平方Bessel函数调幅后的傅里叶基函数的线性组合。结合Bessel函数的频谱特性,分析了轨道角动量态与方位角变量之间的近似对偶关系,利用逆投影和滤波-傅里叶变换方法进行了成像处理。仿真实验表明,电磁涡旋对雷达目标具有方位向成像的潜力。本文的研究可为新体制的雷达设计、目标识别技术的发展提供参考和借鉴。     

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capa-bility for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of task-decomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV's control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV's flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.     

Analysis of a production system in a general configuration

We consider a production system comprising multiple stations (or workshops) such as an entry station, a set of work stations, a central station, and an exit station, which are arranged in a general configuration. A worker (or a vehicle tool) is assigned to each station, who sends a part from the station to the destination station according to the required process path of the part. Any part is allowed to visit a work station more than once if its process path requires. We propose a new control strategy with the push policy for instructing each worker to send a part and the kanban mechanism for controlling the work‐in‐process (WIP) in each work station. As all work stations have limited local buffers, the central station is used for storing blocked parts temporarily. Such a production system is modeled as an open queueing network in a general configuration with a Markovian part sending policy and a machine no blocking mechanism. The queueing network is analytically characterized. Some important performance measures are compared with other control strategies. A semi‐open decomposition approach is applied to the queueing network for computing the blocking probabilities when parts arrive at the work stations. An algorithm is developed based on the semi‐open decomposition approach. Numerical experiments show the quality of the solutions obtained by the algorithm as well as a property of a performance measure. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 128–143, 2001     

基于加权动态规划和航迹关联的小目标检测技术

低信噪比下运动小目标的检测一直是成像目标检测中的一个热点问题。提出了一种新的小目标检测算法,采用方向加权的动态规划算法和二值航迹关联,克服了低信噪比下目标机动和传感器的不稳定对小目标检测的影响,实现了对深空背景下运动方式任意、速度最大达1像素/帧的弱小运动目标的有效检测。     

Search techniques for a nonlinear multiprocessor scheduling problem

The problem of assigning computer program modules to functionally similar processors in a distributed computer network is investigated. The modules of a program must be assigned among processors in such a way as to minimize interprocessor communication while taking advantage of affinities of certain modules to particular processors. This problem is formulated as a zero-one quadratic programming problem, but is more conveniently modeled as a directed acyclic search graph. The model is developed and a backward shortest path labeling algorithm is given that produces an assignment of program modules to processors. A non-backtracking branch-and-bound algorithm is described that uses a local neighborhood search at each stage of the search graph.     

Finding the Kth shortest path in a time‐schedule network

We consider the problem of finding the Kth shortest path for a time‐schedule network, where each node in the network has a list of prespecified departure times, and departure from the node can take place only at one of these departure times. We develop a polynomial time algorithm independent of K for finding the Kth shortest path. The proposed algorithm constructs a map structure at each node in the network, using which we can directly find the Kth shortest path without having to enumerate the first K − 1 paths. Since the same map structure is used for different K values, it is not necessary to reconstruct the table for additional paths. Consequently, the algorithm is suitable for directly finding multiple shortest paths in the same network. Furthermore, the algorithm is modified slightly for enumerating the first K shortest paths and is shown to have the lowest possible time complexity under a condition that holds for most practical networks. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2005.