复杂电磁环境下基于目标动特性的微波成像技术

发布时间：2018-01-16 16:29

  本文关键词：复杂电磁环境下基于目标动特性的微波成像技术　出处：《电子科技大学》2014年硕士论文　论文类型：学位论文

  更多相关文章： 穿墙雷达 相干成像 动目标检测 压缩感知

【摘要】：在现代城市战场、反恐突击、灾后救援等行动中,穿墙雷达(Through-the-Wall Radar,TWR)能够对隐藏在掩体、建筑物、废墟等环境下的人员进行非侵入式的探测与成像,作为重要的现场态势感知工具,为后续行动提供了信息支撑,增加了行动的有效性,因此,其相关技术的研究具有重要的理论意义和应用价值。由于TWR必须满足高空域分辨力要求,其天线孔径、信号带宽都在沿着更大、更宽的趋势发展,而因此带来的信号处理问题也不断凸显。使用传统成像方法需要对信号进行高速采样,增加了采样系统的压力,同时高采样速率所带来的大数据量将形成对处理系统的二次负担,因此限制了TWR的性能和发展。针对上述问题,本文从一发多收(Single-Input Multi-Output,SIMO)TWR的工作原理和信号传播的物理过程开始,重点研究两个科学问题:墙体散射影响及补偿办法、目标动特性在成像过程中的应用。以下为主要研究内容:1.研究了基于一发多收的合成孔径雷达成像技术,给出了数值仿真结果并讨论了系统结构与参数变化对成像效果的影响;2.研究了传统TWR工作原理并分析了墙壁对于电磁波的衰减、折射等影响,建立了TWR回波模型,针对具有复杂散射特性的墙体,本文研究了一种改进的基于最小化熵的墙体厚度估计方法。该方法利用电磁波在墙壁内的折射模型,采用两组天线阵列的回波数据对墙体参数进行估计,并给出基于后向投影(Back Projection,BP)算法的静目标成像及动目标检测机制。数值仿真证实该方法可有效的估计墙壁参数,显著提升了TWR成像中对目标的聚焦与成像性能。3.为了降低探测系统在信号采样率上的要求,本文充分利用变化检测后的动目标在成像空间上具有的稀疏性,结合墙壁参数建立了TWR中目标的稀疏表示模型,提出一种基于目标动特性的压缩感知(Compressed Sensing,CS)成像方法,通过该方法能够以远小于奈奎斯特采样定理的采样率获取回波并准确恢复动目标的成像信息,并通过数值仿真验证了方法的有效性,同时给出了系统配置和采样参数与成像性能之间的分析与结论。
[Abstract]:In modern urban battlefields, anti-terror raids, post-disaster rescue operations, wall-piercing radars can be hidden on bunkers, buildings, and so on. As an important field situational perception tool, the personnel under the ruins and other environments conduct non-intrusive detection and imaging, which provides information support for the follow-up action and increases the effectiveness of the action. Because TWR must meet the requirement of high spatial resolution, its antenna aperture and signal bandwidth are developing along the trend of larger and wider. Because of this, the problem of signal processing is becoming more and more prominent. The traditional imaging method needs to sample the signal at high speed, which increases the pressure of the sampling system. At the same time, the large amount of data brought by high sampling rate will form a secondary burden on the processing system, which limits the performance and development of TWR. This paper begins with the working principle of Single-Input Multi-Output SIMOTWR and the physical process of signal propagation. This paper focuses on two scientific problems: the wall scattering effect and the compensation method. The application of target dynamic characteristics in imaging process. The following is the main research content: 1. The synthetic Aperture Radar (SAR) imaging technology based on multiple transmitters and multiple receivers is studied. The numerical simulation results are given and the influence of the system structure and parameters on the imaging effect is discussed. 2. The traditional TWR working principle is studied and the influence of the wall on the attenuation and refraction of electromagnetic wave is analyzed. The TWR echo model is established for the wall with complex scattering characteristics. In this paper, an improved method of wall thickness estimation based on minimization entropy is proposed, which uses the refraction model of electromagnetic wave in the wall and two sets of antenna array echo data to estimate the wall parameters. The mechanism of static target imaging and moving target detection based on back projection back projection BPalgorithm is presented. Numerical simulation shows that the method can effectively estimate wall parameters. In order to reduce the requirement of signal sampling rate in the detection system, the focusing and imaging performance of the target in TWR imaging is improved significantly. In this paper, we make full use of the sparsity of the moving target in the imaging space after change detection, and establish the sparse representation model of the target in TWR combined with the wall parameters. A compressed sensing CS-based imaging method based on target dynamic characteristics is proposed. By using this method, the echo can be obtained at a sampling rate much less than that of Nyquist sampling theorem, and the imaging information of moving target can be recovered accurately, and the validity of the method is verified by numerical simulation. The analysis and conclusion between system configuration, sampling parameters and imaging performance are also given.
【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】：TN957.52
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本文编号：1433921