相控阵雷达系统实时任务负载分配仿真研究

发布时间：2018-04-14 11:41

本文选题：任务划分 + 分布式负载平衡　；参考：《电子科技大学》2014年硕士论文

【摘要】：随着科技发展,相控阵雷达负载任务越来越重,雷达系统需要处理的数据量急剧增加,计算难度增大,但系统对实时性的要求没有降低。采用单个处理器处理雷达高负载任务已经难以满足相控阵雷达任务实时性的需求。为了快速高效处理相控阵雷达系统负载任务,采用分布式系统并行处理思想,利用调度算法将雷达可并行任务指派到客户机并行处理,降低任务完成时间,满足雷达系统实时性要求;通过调度算法对任务合理调度,充分利用系统资源,提高分布式负载平衡率。据此,对相控阵雷达任务、调度环境建立数学模型,优化指派算法和粒子群算法,并将两种算法分别应用于任务调度中,研究任务的实时调度和负载分配技术,具体研究工作如下:1.相控阵雷达负载任务和分布式调度环境建模,确立两者间映射关系。鉴于任务串行和并行处理差异,雷达任务划分成通信开销小、适于在分布式系统中调度的子任务,并通过任务相关性体现子任务间的数据传输和通信。从任务相关性、复杂度和任务量三方面建立雷达负载任务数学模型。选择“服务器-客户机”主从式调度环境,分别对客户机和通信网络建立数学模型。确立相控阵雷达负载任务在分布式异构系统中调度的目标函数和约束条件,确立任务与环境映射关系。2.提出一种基于指派问题和匈牙利算法的优化指派算法。鉴于指派算法中指派策略导致客户机空闲等待时间过长或通信消耗过大、匈牙利算法会陷入死循环问题,提出优化指派算法:利用任务量作为指派标准,平衡客户机空闲时间和通信消耗之间的矛盾;以任务累积量与阈值之间大小比较作为是否指派任务到客户机的标准,调整系统负载平衡率;以任务完成时间和负载平衡率两个指标为目标,避免匈牙利算法陷入死循环。通过数值仿真和算法对比,验证优化算法避免了匈牙利算法陷入死循环,缩短了任务完成时间,极大地改善了系统负载平衡。3.提出一种基于离散粒子群算法的优化算法。针对粒子群算法收敛速度快但易陷入局部最优解的缺陷,提出粒子群优化算法:按效率矩阵转化概率初始化粒子,增加粒子多样性;通过迭代次数自适应调整惯性系数;将任务完成时间和负载平衡率的加权作为适应度函数,通过适应度函数值负反馈给学习因子,双重调整全局与局部搜索,减少算法陷入局部最优解的风险。数值仿真验证了算法对收敛速度和避免算法陷入局部最优解的有效性,减少了任务完成时间,提高了系统负载平衡率。4.系统实现。将优化指派算法与粒子群优化算法分别置于完整的雷达仿真系统中进行仿真,验证了算法在仿真系统中的优化效果。
[Abstract]:With the development of science and technology, the task of phased array radar is becoming more and more heavy. The amount of data needed to be processed by radar system increases sharply, and the calculation difficulty increases, but the requirement of real-time performance is not reduced.It is difficult to meet the real-time requirement of phased array radar task by using a single processor to deal with high-load task.In order to deal with the phased array radar system load task quickly and efficiently, the distributed system parallel processing idea is adopted, and the radar parallel task is assigned to the client parallel processing by using the scheduling algorithm, which reduces the task completion time.It can meet the real-time requirements of radar system and make full use of system resources to improve the distributed load balancing rate.Based on this, the mathematical model of phased array radar task scheduling environment is established, the assignment algorithm and particle swarm optimization algorithm are optimized, and the two algorithms are applied to task scheduling, and the real-time task scheduling and load allocation techniques are studied.The specific research work is as follows: 1.Based on the modeling of phased array radar load task and distributed scheduling environment, the mapping relationship between them is established.Due to the difference between serial and parallel processing, radar tasks are divided into sub-tasks with low communication overhead, which are suitable for scheduling in distributed systems, and the data transmission and communication among sub-tasks are reflected by task correlation.The mathematical model of radar load task is established from three aspects: task correlation, complexity and task quantity.Selecting the "server-client" master and slave scheduling environment, the mathematical models of client and communication network are established.The objective function and constraint condition of phased array radar load task scheduling in distributed heterogeneous system are established, and the mapping relation between task and environment is established.An optimal assignment algorithm based on assignment problem and Hungarian algorithm is proposed.In view of the fact that the assignment strategy in the assignment algorithm results in too long idle waiting time or too much communication consumption, the Hungarian algorithm will fall into a dead-loop problem. This paper proposes an optimized assignment algorithm, which uses the amount of task as the assignment criterion.Balancing the contradiction between client idle time and communication consumption, adjusting system load balancing rate by comparing the size of task accumulation and threshold as the criterion of whether to assign task to client;Aiming at task completion time and load balancing rate, the Hungarian algorithm is avoided from falling into a dead cycle.Through numerical simulation and algorithm comparison, it is verified that the optimization algorithm avoids Hungary algorithm from falling into dead-cycle, shortens the time of task completion and greatly improves the system load balance. 3.An optimization algorithm based on discrete particle swarm optimization (DPSO) is proposed.Aiming at the defect of particle swarm optimization (PSO), which converges fast but is easy to fall into local optimal solution, particle swarm optimization (PSO) algorithm is proposed: initializing particles according to efficiency matrix transformation probability, increasing particle diversity, adjusting inertia coefficient adaptively by iterating times;The weighting of task completion time and load balancing rate is taken as fitness function. By negative feedback of fitness function value to learning factor, global and local search are adjusted to reduce the risk of the algorithm falling into local optimal solution.Numerical simulation shows that the algorithm is effective to convergence speed and avoid the algorithm falling into local optimal solution, reduces the task completion time and improves the load balancing rate of the system.System realization.The optimization assignment algorithm and particle swarm optimization algorithm are simulated in the complete radar simulation system respectively, and the optimization effect of the algorithm in the simulation system is verified.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN958.92

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