本文選題：穿墻雷達成像　切入點：壓縮感知　出處：《南京理工大學》2017年碩士論文　論文類型：學位論文

【摘要】：穿墻雷達能夠對隱藏在墻體或建筑物后的目標進行探測和成像,在災后救援、反恐作戰(zhàn)等諸多領域都具有廣泛的應用。穿墻雷達成像作為一種新的復雜環(huán)境下的雷達成像技術,在諸多方面仍面臨挑戰(zhàn)。一方面,高分辨成像需求增加了系統復雜度(如大帶寬、多天線等),而復雜的系統導致大量的測量數據,給系統的存儲、傳輸、處理等環(huán)節(jié)造成了很大負擔。另一方面,由于墻體對電磁波的反射作用,接收機會收到來自多個傳播路徑的目標回波,由此形成的多徑效應會在重建圖像中產生不期望的虛像,從而影響成像質量。針對上述問題,本文主要研究基于壓縮感知理論的穿墻雷達高分辨成像技術和多徑抑制技術。本文在利用穿墻雷達接收信號所呈現的組稀疏性基礎上,主要開展基于組稀疏性的穿墻雷達成像技術研究。為有效利用穿墻雷達的組稀疏性,本文采用貝葉斯壓縮感知實現了低數據量下的高分辨成像。穿墻雷達的多徑效應同樣呈現組稀疏特性,本文提出了一種基于組稀疏的多徑分集穿墻雷達成像方法。本論文主要工作如下:(1)簡述基于組稀疏壓縮感知的穿墻雷達成像理論框架。本文在介紹穿墻雷達成像的基本原理和壓縮感知理論基礎上,針對穿墻雷達接收信號模型,根據其具有組稀疏性的特點,研究了基于組稀疏性的穿墻雷達成像方法,并利用兩種組稀疏重構算法,可分離逼近稀疏重構法(SpaRSA)和塊正交匹配追蹤法(BOMP),驗證了基于組稀疏性的穿墻雷達成像性能。(2)發(fā)展基于貝葉斯壓縮感知的組稀疏穿墻雷達成像技術。為有效利用穿墻雷達成像的組稀疏性,本文將貝葉斯壓縮感知理論應用于組稀疏穿墻雷達成像,在構建組稀疏貝葉斯模型基礎上,采用復多任務貝葉斯壓縮感知方法(CMT-BCS)迭代更新貝葉斯模型參數和重構圖像,從而實現組稀疏穿墻雷達成像。仿真結果表明,基于貝葉斯壓縮感知的穿墻雷達成像能夠準確地對墻后場景圖像進行重構,并且計算效率較高。(3)提出基于組稀疏貝葉斯壓縮感知的多徑分集穿墻雷達成像技術。針對穿墻雷達多徑效應問題,本文在深入研究穿墻雷達多徑傳播模式基礎上,揭示其同樣具有組稀疏性,因此有效利用多徑信號的組稀疏性,不僅可抑制多徑所產生的虛像,還可提高穿墻雷達的成像性能。仿真實驗結果表明基于組稀疏壓縮感知的多徑分集穿墻雷達成像在穿墻多徑環(huán)境下仍能夠準確的重構場景中的目標,同時有效地抑制了虛像,取得了高質量的成像結果。
[Abstract]:Wall-penetrating radar can detect and image targets hidden behind walls or buildings. It has been widely used in many fields, such as post-disaster rescue, anti-terrorist warfare, etc. As a new radar imaging technology in complex environment, penetrating wall radar imaging is a kind of radar imaging technology. On the one hand, high resolution imaging requirements increase system complexity (such as large bandwidth, multiple antennas, etc.), while complex systems lead to a large amount of measurement data, storage and transmission to the system. On the other hand, because of the reflection of the wall to the electromagnetic wave, the receiver will receive the target echo from multiple propagation paths, and the resulting multipath effect will produce an unwanted virtual image in the reconstructed image. In order to affect the imaging quality, this paper mainly studies the high-resolution imaging technology and multi-path suppression technology based on the compression sensing theory. In this paper, based on the group sparsity of the received signal from the penetrating radar, we mainly study the high resolution imaging technology and the multi-path suppression technology based on the compression perception theory. In order to make effective use of the thinness of penetrating wall radar, the imaging technology of penetrating wall radar based on group sparsity is studied. In this paper, Bayesian compression sensing is used to realize high resolution imaging under low data volume. The multipath effect of wall-penetrating radar is also sparse. In this paper, we propose a multipath diversity penetrating wall radar imaging method based on sparse group. The main work of this thesis is as follows: 1) briefly introduce the theoretical framework of penetrating wall radar imaging based on sparse compression sensing. In this paper, we introduce the imaging of penetrating wall radar. On the basis of the theory of compression perception, Aiming at the receiving signal model of penetrating radar, according to its characteristics of group sparsity, the imaging method of penetrating radar based on group sparsity is studied, and two sparse reconstruction algorithms are used. Separable approximation sparse reconstruction method (SpaRSA) and block orthogonal matching tracking method (BOMPA) are used to verify the imaging performance of wall penetrating radar based on group sparsity. The development of group sparse penetrating radar imaging technology based on Bayesian compression sensing is presented. Sparse group of wall radar imaging, In this paper, Bayesian compression sensing theory is applied to group sparse wall radar imaging. On the basis of constructing group sparse Bayesian model, complex multitask Bayesian compression sensing method (CMT-BCSS) is used to iteratively update Bayesian model parameters and reconstruct images. The simulation results show that the radar imaging based on Bayesian compression perception can accurately reconstruct the image of the back-wall scene. Furthermore, a new multi-path diversity penetrating radar imaging technique based on sparse Bayesian compression sensing is proposed. Aiming at the multipath effect of the penetrating radar, the multipath propagation mode of the penetrating radar is studied in this paper. It is revealed that the multipath signal is also group sparse. Therefore, using the group sparsity of multipath signal effectively can not only suppress the virtual image produced by multipath. The simulation results show that the multipath diversity penetrating radar imaging based on sparse compression sensing can reconstruct the target of the scene accurately in the multi-path environment, and effectively suppress the virtual image at the same time. High quality imaging results are obtained.
【學位授予單位】：南京理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN957.52

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本文編號：1578916