基于智能算法的分布式MIMO雷达布站研究

发布时间：2018-07-11 21:54

本文选题：MIMO雷达 + 遗传算法　；参考：《电子科技大学》2017年硕士论文

【摘要】：多输入多输出(MIMO)雷达作为一种新体制雷达,因其良好的空间分集增益和多路增益,使其在克服目标雷达截面积(RCS)闪烁及抗干扰等方面具有优势。其中分布式MIMO雷达拥有广布的天线,其性能很大程度上依赖于雷达天线的位置,若不对其雷达站点进行优化布站,其性能将会受到限制。分布式MIMO雷达布站要同时考虑多个天线的位置,是一个复杂高维问题,鉴于智能算法在解决这类问题上的优势,因此基于智能算法的分布式MIMO雷达布站问题也成为国内外学者的研究热点。以往的研究未有效考虑变化的监视需求,未有效利用雷达网络相关的大数据信息,往往难以满足实际的监视需求。本文针对分布式MIMO雷达布站的实际需求,在智能算法求解的基础上,研究了基于变化监视需求和基于大数据的布站方法,具体如下:1.针对分布式MIMO雷达布站问题,确立了以覆盖率为优化指标的问题模型,将监视区域离散化为多个分辨单元,若雷达系统对该单元的检测概率达到某一门限,则认为该单元被覆盖,在此基础上推导出了检测概率的简化表达式。2.针对变化的监视需求,研究了一种基于遗传算法的MIMO雷达动态部署方法,并对该方法与穷举法进行了计算量上的对比分析。仿真实验证实我们算法不仅拥有良好的布站效果且在计算量上相比穷举法具有巨大优势。3.针对复杂的布站环境,研究了环境背景数据(包括高程数据,城市未来规划数据等)的获取,并研究了基于环境背景数据的布站问题,最后利用仿真实验验证了该方法的实效性。4.针对监视区域目标的活动规律,研究了基于雷达历史回波数据的监视区域目标热度图构建方法,在此基础上,研究了基于目标热度图的布站算法,最后仿真实验证实了基于目标热度图布站算法的有效性。以上方法的实效性均通过仿真实验验证,仿真结果表明,所研究方法能够有效解决布站问题的实际需求,具有很大的实际意义。
[Abstract]:As a new type of radar, multiple input multiple output (MIMO) radar has advantages in overcoming radar cross sectional area (RCS) flicker and anti-jamming due to its good spatial diversity gain and multi-channel gain. Distributed MIMO radar has a wide range of antennas and its performance depends largely on the position of the radar antenna. If the radar stations are not optimized its performance will be limited. Distributed MIMO radar stations need to consider the location of multiple antennas at the same time, which is a complex high-dimensional problem. In view of the advantages of intelligent algorithms in solving this kind of problems, Therefore, the distributed MIMO radar station placement based on intelligent algorithm has become a hot research topic at home and abroad. Previous studies have not effectively considered the changing requirements of surveillance, and have not effectively used the big data information related to radar networks, so it is often difficult to meet the actual requirements of surveillance. In this paper, according to the actual demand of distributed MIMO radar stations, based on the solution of intelligent algorithm, the method based on the requirement of change monitoring and the method based on big data is studied, which is as follows: 1. To solve the problem of distributed MIMO radar station placement, a problem model with coverage rate as an optimization index is established. The monitoring area is discretized into several resolution units, if the detection probability of the unit reaches a certain threshold. The simplified expression. 2. 2 of the detection probability is derived on the basis of the assumption that the unit is covered. A dynamic deployment method of MIMO radar based on genetic algorithm is studied and compared with exhaustive method. The simulation results show that our algorithm not only has good station layout effect, but also has a great advantage over exhaustive method. In this paper, the acquisition of environmental background data (including elevation data, urban future planning data, etc.) and the problem of station layout based on environmental background data are studied. Finally, the effectiveness of the method is verified by simulation experiments. 4. In view of the activity rule of the target in the surveillance area, the method of constructing the heat map of the target based on the radar historical echo data is studied. On the basis of this, the algorithm of station placement based on the heat map of the target is studied. Finally, the simulation results show the effectiveness of the algorithm based on the thermal map of the target. The effectiveness of the above methods is verified by simulation experiments. The simulation results show that the proposed method can effectively solve the actual demand of the problem of station distribution and has great practical significance.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;TN958

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