本文選題：多雷達(dá)數(shù)據(jù)融合成像 + 信號稀疏表示��； 參考：《南京理工大學(xué)》2014年碩士論文

【摘要】：由于單雷達(dá)成像系統(tǒng)的分辨率受到信號帶寬和相干積累角的約束,近年來,多雷達(dá)數(shù)據(jù)融合技術(shù)作為一種新興的雷達(dá)成像技術(shù),在軍事上得到了重視并擁有著廣闊的應(yīng)用前景。多雷達(dá)數(shù)據(jù)融合技術(shù)是一種綜合不同視角、不同頻帶雷達(dá)回波數(shù)據(jù),利用信號處理的方法獲得高精度目標(biāo)模型參數(shù)的技術(shù),它突破了單雷達(dá)分辨率的約束,在成像過程中可獲得更高分辨率的清晰圖像。本文主要討論了高頻區(qū)雷達(dá)的數(shù)據(jù)融合問題。利用幾何繞射理論模型,雷達(dá)數(shù)據(jù)融合問題可以轉(zhuǎn)化為信號稀疏表示問題,信號稀疏表示方法作為一種有效的數(shù)據(jù)分析方法,將其應(yīng)用于雷達(dá)成像處理中,可準(zhǔn)確估計出目標(biāo)散射中心參數(shù),大幅提高最終成像質(zhì)量,便于后續(xù)的分析和處理。本文主要包括以下四部分內(nèi)容： 第一部分主要介紹了雷達(dá)數(shù)據(jù)融合技術(shù)的理論基礎(chǔ),包括目標(biāo)電磁散射模型的建立,信號稀疏表示的相關(guān)理論。 第二部分詳細(xì)說明了同視角多頻帶雷達(dá)數(shù)據(jù)融合技術(shù)。首先給出了一維雷達(dá)回波的信號稀疏表示模型,然后針對多子帶觀測情況進(jìn)行了分析,選擇使用稀疏貝葉斯學(xué)習(xí)方法求解信號稀疏表示問題,并分別詳細(xì)介紹了期望最大化方法、求導(dǎo)方法和快速邊緣似然函數(shù)最大化方法三種求解超參數(shù)的方法。 第三部分主要分析了一種基于信號稀疏表示的相干配準(zhǔn)方法。在第一部分信號稀疏表示相關(guān)理論的基礎(chǔ)上,基于幅相補(bǔ)償參數(shù)的稀疏特性,對引起兩部雷達(dá)之間不相干的固定相移和線性相移進(jìn)行估計,在提高了估計精度的同時使算法更具魯棒性。 第四部分重點(diǎn)介紹了多視角多頻帶雷達(dá)數(shù)據(jù)融合技術(shù)。在給出目標(biāo)散射場二維模型的基礎(chǔ)上,構(gòu)建了多視角多頻帶雷達(dá)數(shù)據(jù)的信號稀疏表示模型,最終用稀疏貝葉斯學(xué)習(xí)方法對該信號稀疏表示問題進(jìn)行求解,并用仿真算例驗證了該方法的有效性。
[Abstract]:Because the resolution of single radar imaging system is constrained by signal bandwidth and coherent accumulation angle, in recent years, as a new radar imaging technology, multi-radar data fusion technology has been paid attention to in military and has a broad application prospect. Multi-radar data fusion technology is a kind of technology that synthesizes radar echo data of different visual angle and different frequency band and obtains high precision target model parameters by signal processing method. It breaks through the constraint of single radar resolution. A clear image with higher resolution can be obtained during the imaging process. This paper mainly discusses the data fusion of high frequency radar. Using the geometric diffraction theory model, the radar data fusion problem can be transformed into the signal sparse representation problem. As an effective data analysis method, the signal sparse representation method is applied to radar imaging processing. The scattering center parameters of the target can be estimated accurately, and the final imaging quality can be greatly improved, which is convenient for subsequent analysis and processing. This paper mainly includes the following four parts: The first part mainly introduces the theory foundation of radar data fusion technology, including the establishment of target electromagnetic scattering model and the theory of signal sparse representation. In the second part, the data fusion technology of multi-band radar with same view angle is described in detail. Firstly, the signal sparse representation model of one-dimensional radar echo is given, then the multi-subband observation is analyzed, and the sparse Bayesian learning method is chosen to solve the signal sparse representation problem. Three methods to solve the hyperparameter are introduced in detail, including the expected maximization method, the derivative method and the fast edge likelihood function maximization method. In the third part, a coherent registration method based on sparse signal representation is analyzed. Based on the correlation theory of signal sparse representation and the sparse characteristic of amplitude and phase compensation parameters, the stationary phase shift and linear phase shift which cause incoherence between two radars are estimated. The estimation accuracy is improved and the algorithm is more robust. The fourth part focuses on the multi-view multi-band radar data fusion technology. Based on the two-dimensional model of target scattering field, the signal sparse representation model of multi-view and multi-band radar data is constructed. Finally, the sparse Bayesian learning method is used to solve the signal sparse representation problem. The effectiveness of the method is verified by a simulation example.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN957.52

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