本文關(guān)鍵詞：復(fù)雜電磁環(huán)境下基于目標(biāo)動(dòng)特性的微波成像技術(shù)　出處：《電子科技大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 穿墻雷達(dá) 相干成像 動(dòng)目標(biāo)檢測(cè) 壓縮感知

【摘要】：在現(xiàn)代城市戰(zhàn)場(chǎng)、反恐突擊、災(zāi)后救援等行動(dòng)中,穿墻雷達(dá)(Through-the-Wall Radar,TWR)能夠?qū)﹄[藏在掩體、建筑物、廢墟等環(huán)境下的人員進(jìn)行非侵入式的探測(cè)與成像,作為重要的現(xiàn)場(chǎng)態(tài)勢(shì)感知工具,為后續(xù)行動(dòng)提供了信息支撐,增加了行動(dòng)的有效性,因此,其相關(guān)技術(shù)的研究具有重要的理論意義和應(yīng)用價(jià)值。由于TWR必須滿(mǎn)足高空域分辨力要求,其天線孔徑、信號(hào)帶寬都在沿著更大、更寬的趨勢(shì)發(fā)展,而因此帶來(lái)的信號(hào)處理問(wèn)題也不斷凸顯。使用傳統(tǒng)成像方法需要對(duì)信號(hào)進(jìn)行高速采樣,增加了采樣系統(tǒng)的壓力,同時(shí)高采樣速率所帶來(lái)的大數(shù)據(jù)量將形成對(duì)處理系統(tǒng)的二次負(fù)擔(dān),因此限制了TWR的性能和發(fā)展。針對(duì)上述問(wèn)題,本文從一發(fā)多收(Single-Input Multi-Output,SIMO)TWR的工作原理和信號(hào)傳播的物理過(guò)程開(kāi)始,重點(diǎn)研究?jī)蓚�(gè)科學(xué)問(wèn)題:墻體散射影響及補(bǔ)償辦法、目標(biāo)動(dòng)特性在成像過(guò)程中的應(yīng)用。以下為主要研究?jī)?nèi)容:1.研究了基于一發(fā)多收的合成孔徑雷達(dá)成像技術(shù),給出了數(shù)值仿真結(jié)果并討論了系統(tǒng)結(jié)構(gòu)與參數(shù)變化對(duì)成像效果的影響;2.研究了傳統(tǒng)TWR工作原理并分析了墻壁對(duì)于電磁波的衰減、折射等影響,建立了TWR回波模型,針對(duì)具有復(fù)雜散射特性的墻體,本文研究了一種改進(jìn)的基于最小化熵的墻體厚度估計(jì)方法。該方法利用電磁波在墻壁內(nèi)的折射模型,采用兩組天線陣列的回波數(shù)據(jù)對(duì)墻體參數(shù)進(jìn)行估計(jì),并給出基于后向投影(Back Projection,BP)算法的靜目標(biāo)成像及動(dòng)目標(biāo)檢測(cè)機(jī)制。數(shù)值仿真證實(shí)該方法可有效的估計(jì)墻壁參數(shù),顯著提升了TWR成像中對(duì)目標(biāo)的聚焦與成像性能。3.為了降低探測(cè)系統(tǒng)在信號(hào)采樣率上的要求,本文充分利用變化檢測(cè)后的動(dòng)目標(biāo)在成像空間上具有的稀疏性,結(jié)合墻壁參數(shù)建立了TWR中目標(biāo)的稀疏表示模型,提出一種基于目標(biāo)動(dòng)特性的壓縮感知(Compressed Sensing,CS)成像方法,通過(guò)該方法能夠以遠(yuǎn)小于奈奎斯特采樣定理的采樣率獲取回波并準(zhǔn)確恢復(fù)動(dòng)目標(biāo)的成像信息,并通過(guò)數(shù)值仿真驗(yàn)證了方法的有效性,同時(shí)給出了系統(tǒng)配置和采樣參數(shù)與成像性能之間的分析與結(jié)論。
[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.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN957.52
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本文編號(hào)：1433921