本文選題：合成　切入點(diǎn)：孔徑雷達(dá)　出處：《中國(guó)科學(xué)技術(shù)大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：合成孔徑雷達(dá)利用小天線在平臺(tái)上的運(yùn)動(dòng)來(lái)合成一個(gè)等效的長(zhǎng)天線,在沒(méi)有增加實(shí)際天線孔徑的情況下提升了成像分辨率,對(duì)于傳統(tǒng)的雷達(dá)成像來(lái)說(shuō)是一個(gè)歷史性的突破,使得合成孔徑雷達(dá)成像無(wú)論在軍用還是民用領(lǐng)域都得到了廣泛應(yīng)用。隨著成像的目標(biāo)場(chǎng)景越來(lái)越大,導(dǎo)致需要處理的數(shù)據(jù)量也隨之增加,給硬件系統(tǒng)帶來(lái)很大壓力。而實(shí)際成像場(chǎng)景往往是稀疏的或具有某種結(jié)構(gòu)性,因此可以用壓縮感知理論進(jìn)行處理。壓縮感知理論利用信號(hào)中信息的冗余降低采樣率,并證明當(dāng)滿足一定條件時(shí)可以利用稀疏優(yōu)化算法從欠采樣的數(shù)據(jù)中重構(gòu)出原信號(hào)。和傳統(tǒng)的信號(hào)采樣理論相比,壓縮感知算法將信號(hào)采樣和壓縮的步驟合并到一起,直接進(jìn)行欠采樣,減少了數(shù)據(jù)量,降低了數(shù)據(jù)存儲(chǔ)和傳輸?shù)膲毫�。壓縮感知SAR成像近年來(lái)吸引了眾多學(xué)者的關(guān)注。本文研究了壓縮感知SAR成像的重構(gòu)算法,利用傳統(tǒng)的匹配濾波方法對(duì)重構(gòu)算法進(jìn)行優(yōu)化,降低重構(gòu)算法復(fù)雜度。本文首先在緒論中介紹了合成孔徑雷達(dá)的發(fā)展歷史以及所面臨的問(wèn)題,接著引入了壓縮感知理論,簡(jiǎn)要介紹了壓縮感知SAR成像的理論和發(fā)展,在此基礎(chǔ)上還介紹了單比特壓縮感知以及單比特壓縮感知SAR成像理論。本章提出了一些壓縮感知SAR成像中仍然存在的問(wèn)題,并將針對(duì)這些問(wèn)題具體展開(kāi)研究。第二章介紹了壓縮感知理論以及稀疏重構(gòu)算法。本章分析了壓縮感知問(wèn)題成立的條件,介紹了幾種常用的壓縮感知模型以及相應(yīng)的重構(gòu)算法,分析了不同重構(gòu)算法的特點(diǎn),并提出壓縮感知SAR成像中存在的一些限制。第三章針對(duì)壓縮感知SAR成像重構(gòu)算法計(jì)算復(fù)雜度過(guò)高的問(wèn)題提出了優(yōu)化算法,將傳統(tǒng)匹配濾波omega-K算法和壓縮感知算法結(jié)合,提出了一種基于近似代替的低復(fù)雜度壓縮感知SAR成像算法。本章證明了算法的可行性并進(jìn)行了相應(yīng)的理論推導(dǎo),對(duì)時(shí)空復(fù)雜度進(jìn)行了定量分析,利用匹配濾波降低了算法復(fù)雜度,減少了數(shù)據(jù)存儲(chǔ)需求。實(shí)驗(yàn)結(jié)果驗(yàn)證了算法的有效性。第四章分析了上一章中算法在低信噪比情況下成像效果不好的問(wèn)題,提出了一種低復(fù)雜度的單比特壓縮感知SAR成像方法。不僅改善了算法在低信噪比情況下的重構(gòu)性能,也緩解了接收端ADC的壓力。在分析利用omega-K算法降低計(jì)算復(fù)雜度的可行性基礎(chǔ)上,推導(dǎo)了具體計(jì)算過(guò)程。優(yōu)化算法改善了低信噪比下的成像效果,降低了單比特壓縮感知SAR成像算法的時(shí)空復(fù)雜度。實(shí)驗(yàn)結(jié)果驗(yàn)證了算法的有效性。
[Abstract]:Synthetic Aperture Radar (SAR) uses the motion of small antennas on the platform to synthesize an equivalent long antenna, which improves the imaging resolution without increasing the actual antenna aperture, which is a historic breakthrough for traditional radar imaging. Synthetic Aperture Radar (SAR) imaging has been widely used in both military and civil fields. As the target scene becomes larger and larger, the amount of data that needs to be processed increases. The actual imaging scene is often sparse or has some structure, so it can be processed by compression sensing theory, which reduces the sampling rate by using the redundancy of information in the signal. It is proved that the original signal can be reconstructed from the under-sampled data by sparse optimization algorithm when certain conditions are satisfied. Compared with the traditional signal sampling theory, the compression sensing algorithm combines the steps of signal sampling and compression together. Direct under-sampling reduces the amount of data and reduces the pressure of data storage and transmission. Compression sensing SAR imaging has attracted the attention of many scholars in recent years. In this paper, the reconstruction algorithm of compressed sensing SAR imaging is studied. The traditional matched filtering method is used to optimize the reconstruction algorithm to reduce the complexity of the reconstruction algorithm. Firstly, this paper introduces the history and problems of synthetic Aperture Radar (SAR) in the introduction, and then introduces the theory of compressed sensing. In this paper, the theory and development of compressed sensing SAR imaging are briefly introduced, and the theories of single bit compression sensing and single bit compression sensing SAR imaging are also introduced. In this chapter, some problems in compression sensing SAR imaging are presented. In chapter 2, the theory of compressed perception and sparse reconstruction algorithm are introduced. In this chapter, the conditions of the problem are analyzed, and several commonly used compression sensing models and corresponding reconstruction algorithms are introduced. The characteristics of different reconstruction algorithms are analyzed, and some limitations in compression sensing SAR imaging are proposed. In chapter 3, an optimization algorithm is proposed to solve the problem of high computational complexity of compression sensing SAR imaging reconstruction algorithms. Based on the combination of traditional matched filter omega-K algorithm and compression sensing algorithm, a low complexity compression sensing SAR imaging algorithm based on approximate substitution is proposed. The feasibility of the algorithm is proved in this chapter and the corresponding theoretical derivation is given. The complexity of time and space is analyzed quantitatively, and the algorithm complexity is reduced by using matched filter. The experimental results verify the effectiveness of the algorithm. Chapter 4th analyzes the problem of poor imaging performance in the case of low signal-to-noise ratio in the previous chapter. A low complexity single bit compression sensing SAR imaging method is proposed, which not only improves the reconstruction performance of the algorithm in the case of low signal-to-noise ratio (SNR). On the basis of analyzing the feasibility of using omega-K algorithm to reduce the computational complexity, the concrete calculation process is deduced. The optimized algorithm improves the imaging effect at low SNR. The time and space complexity of the single bit compression sensing SAR imaging algorithm is reduced. The experimental results show that the algorithm is effective.
【學(xué)位授予單位】：中國(guó)科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TN957.52

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