【摘要】：隨著我國海洋事業(yè)的蓬勃發(fā)展,對水下成像質(zhì)量要求日漸增高,具備高分辨率成像能力的合成孔徑聲納設(shè)備逐漸成為研究熱點。為了獲得高分辨率成像,通常需要采集大量的水下數(shù)據(jù),會給相應(yīng)的硬件設(shè)備及發(fā)送測量數(shù)據(jù)的通信系統(tǒng)帶來極大的挑戰(zhàn)。因此,研究如何在保證成像質(zhì)量的同時降低所需數(shù)據(jù)量具有重要的現(xiàn)實意義。 壓縮感知理論(Compressed Sensing)利用信號的稀疏特性,可以將無失真重建信號的最小采樣頻率降低到奈奎斯特頻率以下,可用于解決成像數(shù)據(jù)量大的問題。在本文中,主要探討研究了兩種應(yīng)用壓縮感知的合成孔徑成像算法。由于聲納信號的稀疏矩陣位于復(fù)數(shù)域,本文給出了重構(gòu)算法復(fù)數(shù)域的實現(xiàn)形式,最后利用點目標(biāo)的成像仿真實驗驗證了算法性能。 本文的主要工作如下： (1)詳細(xì)介紹了壓縮感知基本理論和基礎(chǔ)知識,主要包括信號的稀疏、非線性測量及原始信號的重建三個部分。研究合成孔徑聲納成像原理及經(jīng)典的距離多普勒成像算法,并完成了四點目標(biāo)的成像仿真； (2)為解決距離向較大數(shù)據(jù)量的問題,研究了具有保相性的距離向壓縮感知的成像算法。實驗結(jié)果顯示,該算法在距離向僅用20%的原始數(shù)據(jù),即可獲得良好的成像效果;算法本身可以起到抑制旁瓣的作用,且具有良好的抗噪性能。為了在不產(chǎn)生方位模糊的前提下獲得更遠(yuǎn)的觀察距離,研究了二維降采樣壓縮感知成像算法,該算法在保證成像質(zhì)量的同時,可進(jìn)一步降低數(shù)據(jù)量； (3)探討兩種壓縮感知成像算法中重建算法的復(fù)數(shù)域?qū)崿F(xiàn)形式,即經(jīng)典算法的分解式復(fù)數(shù)域重構(gòu)形式及可在復(fù)數(shù)域直接求解的SPGL1算法,結(jié)果表明,SPGL1算法不僅可以節(jié)省存儲空間,而且抗噪性能較好。
[Abstract]:With the rapid development of marine industry in China, the requirement of underwater imaging quality is increasing, and synthetic aperture sonar equipment with high resolution imaging capability has gradually become a research hotspot. In order to obtain high resolution imaging, it is usually necessary to collect a large amount of underwater data, which will bring great challenges to the corresponding hardware devices and communication systems that send measurement data. Therefore, it is of great practical significance to study how to reduce the amount of data required while ensuring the imaging quality. Compression sensing theory (Compressed Sensing) can reduce the minimum sampling frequency of distorted reconstructed signal to less than Nyquist frequency, which can be used to solve the problem of large amount of imaging data. In this paper, two synthetic aperture imaging algorithms using compression sensing are studied. Since the sparse matrix of sonar signal is located in the complex field, this paper presents the realization form of the complex domain of reconstruction algorithm. Finally, the performance of the algorithm is verified by the imaging simulation of point target. The main work of this paper is as follows: (1) the basic theory and basic knowledge of compression sensing are introduced in detail, including sparse signal, nonlinear measurement and reconstruction of original signal. The principle of synthetic aperture sonar imaging and the classical range Doppler imaging algorithm are studied, and the imaging simulation of the four-point target is completed. (2) in order to solve the problem of large amount of data in range direction, the imaging algorithm of range compression sensing with phase-preserving property is studied. The experimental results show that the algorithm can obtain a good imaging effect with only 20% of the original data in the distance direction, and the algorithm itself can suppress the sidelobe and has a good anti-noise performance. In order to obtain longer observation distance without azimuth ambiguity, a two-dimensional downsampling compression sensing imaging algorithm is studied, which can further reduce the amount of data while guaranteeing the imaging quality. (3) the complex domain reconstruction of two compression sensing imaging algorithms is discussed, that is, the decomposed complex domain reconstruction form of classical algorithm and the SPGL1 algorithm which can be solved directly in complex field. The results show that, SPGL1 algorithm not only can save storage space, but also has good anti-noise performance.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：P715.5

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本文編號：2339600