本文選題：合成孔徑雷達 + 壓縮感知��； 參考：《西安電子科技大學》2014年碩士論文

【摘要】：高分辨SAR成像一直是研究的重點問題,這幾年興起的高分辨SAR稀疏成像更是備受關(guān)注,這里的稀疏主要是指成像場景中包含少量且很強的散射點,這就是我們后面要討論的高分辨SAR稀疏目標成像。其目的主要是最大程度的減少成像場景的背景雜波、噪聲和旁瓣對目標的干擾,重點關(guān)注稀疏目標的成像質(zhì)量,以達到突出目標,削弱干擾的效果,降低后續(xù)的目標檢測和識別的難度�；贜yquist采樣定理的傳統(tǒng)回波數(shù)據(jù)采樣方法通常獲得全采樣的數(shù)據(jù),導致高分辨SAR的采樣率過高,數(shù)據(jù)量劇增,給數(shù)據(jù)的存儲、傳輸和實時處理帶來了很大的困難。壓縮感知(Compressed Sensing,CS)理論的出現(xiàn),為降低雷達數(shù)據(jù)采樣率,減小雷達平臺硬件端的壓力,改善雷達成像質(zhì)量開辟了新的思路。國內(nèi)外學者將CS理論與SAR成像理論相結(jié)合,在基于距離向或方位向的一維CS成像和二維CS成像應用方面取得了一批研究成果,使稀疏目標場景的成像質(zhì)量有了顯著的改善。但是,CS與SAR成像的結(jié)合還有很多細節(jié)有待進一步深入研究。不同于壓縮感知,低秩矩陣重建理論根據(jù)成像場景的低秩特性,從矩陣的秩的角度對受噪聲干擾和缺損的數(shù)據(jù)進行恢復。本文對這兩種方法在高分辨合成孔徑雷達稀疏目標成像中的應用進行了系統(tǒng)的研究,所取得的研究成果為:1.對CS與高分辨率SAR稀疏目標成像的結(jié)合進行了研究。針對CS成像中存在的觀測矩陣耗費存儲大,重建結(jié)果時間長以及參數(shù)設(shè)置復雜的問題,提出了改進的二維CS的SAR成像模型。首先對原始回波數(shù)據(jù)進行距離徙動(RCM)校正,消除距離向和方位向的二維耦合,然后對觀測場景的距離向和方位向分別建立觀測矩陣進行觀測,這樣可以顯著減少觀測矩陣的存儲量和原始數(shù)據(jù)量,最后,利用改進的迭代硬閾值(IHT)算法對成像場景進行重構(gòu)。我們利用成像場景服從特定分布的先驗知識,簡化了閾值參數(shù)的求取方法,在成像場景稀疏度未知的情況下,仍然可以獲得比較好的二維高分辨稀疏目標成像結(jié)果,仿真和實測數(shù)據(jù)的成像結(jié)果都驗證了本文方法的有效性。2.對低秩矩陣重建在SAR高分辨稀疏目標成像中的應用進行了研究。不同于CS的向量化處理,低秩矩陣重建理論包括矩陣填充(MC)和低秩矩陣恢復或魯棒主成份分析(RPCA)理論依據(jù)矩陣秩的特性,直接針對二維信號矩陣進行處理,我們將傳統(tǒng)的成像算法與低秩矩陣重建理論相結(jié)合,提出了一種新的成像框架。首先,我們證明了距離徙動校正(RCMC)后的原始回波數(shù)據(jù)矩陣的秩與觀測場景的秩相等。由此可知,對于低秩的成像場景,經(jīng)過RCMC的回波數(shù)據(jù)也具有低秩特征,這是MC在SAR數(shù)據(jù)中應用的首要條件。接下來,首先進行距離徙動校正,再利用矩陣填充方法對缺損和受噪聲污染的回波數(shù)據(jù)進行補全和去噪,恢復回波數(shù)據(jù)的低秩特性,并將回波數(shù)據(jù)分解為兩個低維的低秩矩陣,達到降維壓縮的目的。最后,根據(jù)低秩矩陣恢復理論(RPCA)建立新的SAR成像模型,利用加速近似梯度算法(APG)進行重建,重建結(jié)果分為低秩和稀疏的兩部分,其中稀疏分量就是稀疏目標的聚焦結(jié)果。點目標和實測數(shù)據(jù)實驗都驗證了該成像框架的可行性和有效性,并說明了矩陣秩的信息在回波數(shù)據(jù)處理中有豐富的應用,若能進一步充分挖掘和利用回波矩陣和觀測場景矩陣秩的信息,則可以進一步減小原始數(shù)據(jù)壓縮和成像對觀測場景低秩特性的依賴。
[Abstract]:High resolution SAR imaging has always been the focus of research. The high resolution SAR sparse imaging, which has been developed in recent years, is more and more concerned. The sparsity of the high resolution is mainly refers to the small and very strong scattering points in the imaging scene. This is the high resolution SAR sparse target imaging which we have to discuss later. The purpose is to minimize the imaging field to the maximum extent. The background clutter, noise and sidelobe interference to the target, focus on the imaging quality of the sparse target, in order to achieve the target, weaken the interference effect and reduce the difficulty of the subsequent target detection and recognition. The traditional data sampling method based on the Nyquist sampling theorem often obtains the total sampled data, resulting in the sampling of high resolution SAR. The high rate and the increase of data have brought great difficulties to the storage, transmission and real-time processing of data. The emergence of Compressed Sensing (CS) theory has opened a new idea for reducing the sampling rate of radar data, reducing the pressure of the hardware end of the radar platform, and improving the quality of radar imaging. The scholars at home and abroad and the SAR imaging theory On the other hand, a number of research achievements have been achieved in the application of one-dimensional CS imaging and two-dimensional CS imaging based on distance or azimuth. The imaging quality of the sparse target scene has been significantly improved. However, there are many details to be further studied in the combination of CS and SAR imaging. According to the low rank characteristic of the imaging scene, the noise and the defect data are recovered from the rank of the matrix. The application of these two methods in the sparse target imaging of high resolution synthetic aperture radar is systematically studied in this paper. The research results are as follows: 1. the combination of CS and high resolution SAR sparse target imaging In view of the large storage of observation matrix in CS imaging, long reconstruction time and complex parameter setting, an improved two-dimensional CS SAR imaging model is proposed. First, the distance migration (RCM) correction of the original echo data is used to eliminate the two-dimensional coupling between the distance and square bits, and then the distance of the observation scene. The observation matrix is observed by the direction and direction, which can significantly reduce the storage and the original data of the observation matrix. Finally, the improved iterative hard threshold (IHT) algorithm is used to reconstruct the imaging scene. We use the imaging scene to obey the prior knowledge of the specific distribution, and simplify the method of the threshold parameter estimation. When the scene sparsity is unknown, a good two-dimensional high-resolution sparse target imaging results can still be obtained. The results of the simulation and the measured data all verify the effectiveness of the proposed method.2.. The application of the low rank matrix reconstruction in the SAR high resolution sparse target imaging is studied. Different from the vector processing of CS, the low rank is low. The theory of matrix reconstruction includes matrix filling (MC), low rank matrix recovery or robust principal component analysis (RPCA) theory based on the properties of matrix rank, directly dealing with the two-dimensional signal matrix. We combine the traditional imaging algorithm with the low rank matrix reconstruction theory and propose a new imaging framework. First, we prove the distance migration. The rank of the original echo data matrix after the RCMC is equal to the rank of the observed scene. Thus, for low rank imaging scenes, the echo data of the RCMC also have low rank characteristics. This is the primary condition for the application of MC in the SAR data. The echo data of the acoustic pollution are complementing and denoising, restoring the low rank characteristic of the echo data and decomposing the echo data into two low dimensional low rank matrices to achieve the purpose of reducing dimension compression. Finally, a new SAR imaging model is established based on the low rank matrix recovery theory (RPCA), and the accelerated approximate gradient algorithm (APG) is used for reconstruction and the reconstruction results are divided. For the two part of the low rank and sparsity, the sparse component is the focusing result of the sparse target. The experiment of the point target and the measured data verify the feasibility and effectiveness of the imaging frame, and show that the information of the matrix rank is rich in the application of the echo data processing. If the echo matrix and the observation field can be fully exploited and used in one step The information of the rank of the scene matrix can further reduce the dependence of the original data compression and imaging on the low rank characteristics of the observation scene.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN957.52

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