【摘要】：成像雷達能根據(jù)目標(biāo)的電磁散射回波反演目標(biāo)的散射率分布,對于目標(biāo)識別等應(yīng)用具有重要意義。為了更全面精細(xì)地刻畫雷達目標(biāo)散射特性,多維度雷達成像方法,包括被動雙站雷達成像、高分辨全極化雷達成像以及三維雷達成像應(yīng)運而生。新興的壓縮感知理論提供了全新的信號采集框架,能從欠采樣數(shù)據(jù)中精確重構(gòu)原信號。因此,將壓縮感知理論應(yīng)用于雷達成像,有望解決信號采樣率高、數(shù)據(jù)量大以及不完整采樣下成像等雷達所面臨的問題。本文以提高雷達成像能力為目的,以基于壓縮感知理論的雷達成像方法研究為主線,深入研究了被動雙站ISAR成像、高分辨全極化ISAR成像以及三維雷達成像的理論和方法。第一章闡述了論文的研究背景及意義,總結(jié)和歸納了壓縮感知理論以及壓縮感知雷達成像方法的研究現(xiàn)狀,在此基礎(chǔ)上指出了多維度雷達成像所需解決的問題,最后介紹了本文的主要工作和內(nèi)容安排。第二章描述了基于壓縮感知的ISAR成像基本原理。首先對壓縮感知理論的數(shù)學(xué)模型進行了簡要回顧,其次采用頻率步進信號推導(dǎo)了雷達目標(biāo)二維ISAR成像的回波模型,然后將壓縮感知方法應(yīng)用于ISAR成像,探討了兩種稀疏采樣方案,并選用合適的重構(gòu)算法以及字典加密倍數(shù),最后利用稀疏性先驗實現(xiàn)高分辨圖像重構(gòu),仿真數(shù)據(jù)和實測數(shù)據(jù)結(jié)果驗證了壓縮感知ISAR成像方法的有效性,為后續(xù)章節(jié)的研究提供了理論和方法基礎(chǔ)。第三章針對被動雙站ISAR成像中的柵瓣問題和圖像分辨率不高的不足,研究了基于壓縮感知的被動雙站ISAR成像方法。首先基于Batches算法思想推導(dǎo)了基于機會照明源的被動雙站ISAR成像信號模型,指出從統(tǒng)計平均的角度看,最終的成像模型同樣能表達成適于壓縮感知處理的二維矩陣形式;在目標(biāo)稀疏性的先驗信息約束下,通過求解最優(yōu)化問題得到目標(biāo)的散射中心參數(shù),進而計算完成缺失頻帶補償?shù)耐暾麛?shù)據(jù),然后利用距離-多普勒成像算法獲得最終的ISAR像。進一步地,在獲得的散射中心參數(shù)基礎(chǔ)上,根據(jù)信號模型人為地外推測量數(shù)據(jù),使得ISAR成像的分辨率得到增強。仿真數(shù)據(jù)和實測數(shù)據(jù)處理結(jié)果表明該方法能有效降低柵瓣的影響,并且能夠提高成像結(jié)果的分辨率。第四章針對傳統(tǒng)單極化處理方法不能保證散射中心在不同極化下數(shù)量、位置的一致性,研究了基于壓縮感知的高分辨全極化ISAR成像方法。首先指出全極化下的ISAR像具有相同的稀疏性支撐,也就是聯(lián)合稀疏性,從而將全極化ISAR成像問題轉(zhuǎn)化為二維多測量稀疏恢復(fù)問題;然后為了表征這種聯(lián)合稀疏性,定義了兩類混合范數(shù),并且用連續(xù)的高斯函數(shù)近似該混合范數(shù);最后求解由混合范數(shù)約束的最優(yōu)化問題,得到ISAR成像結(jié)果。仿真數(shù)據(jù)、暗室實測數(shù)據(jù)及外場實測數(shù)據(jù)的實驗結(jié)果表明,兩種基于混合范數(shù)優(yōu)化的全極化ISAR成像方法不僅能利用欠采樣數(shù)據(jù)獲得高分辨ISAR像,而且成像結(jié)果中散射中心是對齊的,有利于后續(xù)的目標(biāo)識別等應(yīng)用。第五章研究了基于壓縮感知的三維雷達成像方法,包括干涉ISAR成像和轉(zhuǎn)臺三維ISAR成像兩部分。在干涉ISAR成像中,借鑒第四章的研究思路,認(rèn)為基線對應(yīng)的兩幅ISAR像有相同的稀疏性支撐,進而定義兩類全局稀疏性,并以此為約束求解優(yōu)化問題,獲得高質(zhì)量的ISAR像,進一步作干涉處理即可得到目標(biāo)的三維重構(gòu)結(jié)果。對于三維轉(zhuǎn)臺ISAR成像,針對傳統(tǒng)向量化壓縮感知計算復(fù)雜度高、內(nèi)存消耗大的不足,利用三維數(shù)據(jù)的結(jié)構(gòu)特性提出了降維壓縮感知方法和張量壓縮感知方法,前者將三維觀測數(shù)據(jù)展開為矩陣形式處理而后者直接對三維數(shù)據(jù)進行處理,兩種方法均大大提高了算法的運算效率,并能夠明顯降低內(nèi)存消耗,有望將其應(yīng)用到大尺寸目標(biāo)成像中。點目標(biāo)仿真實驗和電磁軟件計算數(shù)據(jù)結(jié)果表明所提方法能有效獲得目標(biāo)三維像。最后在第六章對全文進行了總結(jié),并展望了下一步的研究工作。
[Abstract]:The imaging radar can obtain the scattering rate distribution of the target according to the target electromagnetic scattering echo, and has important significance for the target recognition and the like. In order to depict radar target scattering characteristic more fully, multi-dimension radar imaging method includes passive double-station radar imaging, high-resolution full-polarization radar imaging and three-dimensional radar imaging. The new compression-sensing theory provides a new signal acquisition framework, which can reconstruct the original signal accurately from under-sampled data. Therefore, the compression-sensing theory is applied to radar imaging, which is expected to solve the problems faced by radar such as high sampling rate, large data volume and incomplete sampling. In order to improve radar imaging capability, this paper studies the theory and method of passive bistatic ISAR imaging, high resolution full polarization ISAR imaging and three-dimensional radar imaging based on the research of radar imaging method based on the theory of compression sensing. The first chapter expounds the background and significance of the thesis, sums up and summarizes the research status of the compression sensing theory and the compression sensing radar imaging method, and then points out the problems needed to solve the multi-dimensional radar imaging, and finally introduces the main work and the content arrangement in this paper. The second chapter describes the basic principle of ISAR imaging based on compression sensing. Firstly, the mathematical model of the compression sensing theory is briefly reviewed, and then the echo model of the radar target two-dimensional ISAR imaging is derived by using the frequency step signal, and then the compression sensing method is applied to ISAR imaging, and two sparse sampling schemes are discussed. In this paper, a suitable reconstruction algorithm and a dictionary encryption multiple are selected, and the validity of the compression-sensing ISAR imaging method is verified by using the sparse priori to realize the reconstruction of the high-resolution image, the simulation data and the measured data. The theoretical and methodological bases are provided for the research of the following chapters. In chapter three, the problem of grating lobe and image resolution in passive bistatic ISAR imaging are studied, and a passive bistatic ISAR imaging method based on compression sensing is studied. Firstly, based on the Batcheles algorithm, a passive bistatic ISAR imaging signal model based on the opportunity illumination source is derived. It is pointed out that the final imaging model can be expressed in a two-dimensional matrix form suitable for the compression sensing process from the statistical average point of view; under the prior information constraint of the target sparsity, By solving the optimization problem, the scattering center parameters of the target are obtained, and then the complete data for completing the missing band compensation is calculated, and then the final ISAR image is obtained by using the distance-Doppler imaging algorithm. Further, based on the obtained scattering center parameters, the measurement data is artificially extrapolated according to the signal model, so that the resolution of ISAR imaging is enhanced. The simulation data and the data processing results show that the method can effectively reduce the influence of the grating lobes, and can improve the resolution of the imaging results. In chapter 4, aiming at the consistency of the number and position of scattering center under different polarization, the method of high resolution full polarization ISAR imaging based on compression sensing is studied. First, it is pointed out that the ISAR image under full polarization has the same sparsity support, that is, the joint sparsity, so that the whole-polarized ISAR imaging problem is converted into two-dimensional multi-measurement sparse recovery problem; then, in order to characterize this joint sparsity, two kinds of mixed norms are defined. and solving the optimization problem constrained by the mixed norm to obtain ISAR imaging results. The experimental results of simulation data, darkroom test data and field measured data show that the two-polarization ISAR imaging method based on hybrid norm optimization can not only obtain the high resolution ISAR image by using the undersampled data, but also the scattering centers in the imaging results are aligned. and is favorable for subsequent target recognition and the like. In chapter five, a three-dimensional radar imaging method based on compression sensing is studied, including interference ISAR imaging and turntable three-dimensional ISAR imaging. In the ISAR imaging, the two ISAR images corresponding to the baseline are considered to have the same sparsity support, and then two kinds of global sparsity are defined, and then the optimization problem is solved by taking the constraint solving optimization problem to obtain the high-quality ISAR image. and further performing interference processing to obtain a three-dimensional reconstruction result of the target. For the three-dimensional turntable ISAR imaging, aiming at the defects of high computational complexity and large memory consumption to the traditional vectorized compression sensing calculation, a method for reducing dimension compression sensing and a tensor compression sensing method are proposed by utilizing the structural characteristics of the three-dimensional data. In the former, the three-dimensional observation data is expanded into matrix form processing and the latter directly processes the three-dimensional data. Both methods greatly improve the operation efficiency of the algorithm, and can obviously reduce the memory consumption and can be applied to large-scale target imaging. The simulation experiment of point target and the calculation data of electromagnetic software show that the proposed method can get the target three-dimensional image effectively. Finally, the paper summarizes the whole thesis in Chapter 6, and looks forward to the next research work.
【學(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN957.52
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本文編號：2297791