本文選題：海雜波　切入點：IPIX　出處：《哈爾濱工業(yè)大學》2014年碩士論文　論文類型：學位論文

【摘要】：海雜波，，來自海洋表面的雷達后向散射回波,海雜波的存在嚴重干擾了雷達對海面目標的檢測和識別能力，因此對海雜波的研究對于雷達系統(tǒng)設(shè)計、雷達信號處理和海洋目標檢測識別都具有非常重要的意義。目前雷達的海面目標檢測技術(shù)廣泛地應(yīng)用于海洋安全、海事救生、監(jiān)控非法走私等領(lǐng)域，具有很高的軍事價值和民用價值。論文正是基于上述背景，展開對高分辨率雷達低掠射角下的實測海雜波數(shù)據(jù)的研究分析。 1)首先研究了海雜波幅度統(tǒng)計特性，針對IPIX相參雷達實測海雜波數(shù)據(jù)展開幅度統(tǒng)計模型的擬合，并對擬合效果進行擬合優(yōu)度檢驗；其次研究了海雜波時間序列、Speckle分量和Texture分量的時間相關(guān)性及去相關(guān)時間。 2) Hilbert-Huang變換是美籍華裔科學家黃鍔在1998年新提出的一種基于信號自身的時頻分析方法，主要包括EMD經(jīng)驗模態(tài)分解和Hilbert譜分析兩個過程。論文中利用相關(guān)系數(shù)法來去除EMD分解過程中產(chǎn)生的虛假模態(tài)函數(shù)分量，并在此基礎(chǔ)上提出了基于EMD分解的多分量信號WVD變換交叉干擾項抑制方法；然后將Hilbert-Huang變換應(yīng)用到IPIX相參雷達實測海雜波數(shù)據(jù)分析處理中，從低頻IMF能量比、IMF模態(tài)能量熵、Hilbert譜脊線粗糙度三個不同角度實現(xiàn)海雜波背景下的微弱目標檢測。 3)基于時間-Doppler速度法和局部幅值統(tǒng)計的思想對IPIX相參雷達實測海雜波數(shù)據(jù)研究分析，實驗結(jié)果表明該兩種方法是有效的，可以在沒有先驗知識的條件下檢測到目標距離單元的所在。 4)實際海面復雜多變，實測數(shù)據(jù)往往包含雷達測量噪聲和來自粗糙海面的動態(tài)環(huán)境噪聲。為了抑制這些噪聲對海雜波數(shù)據(jù)的內(nèi)在特性分析的影響，重點研究了基于小波變換的閾值降噪算法，實驗仿真表明利用小波閾值法對實測數(shù)據(jù)進行降噪處理可以取得較好的降噪效果。 5)針對IPIX相參雷達實測海雜波數(shù)據(jù)處理研究，利用MATLAB軟件設(shè)計其控制界面GUI，并將各種算法嵌入其中，實現(xiàn)人機交互。
[Abstract]:Sea clutter, radar backscattering echo from ocean surface, the existence of sea clutter seriously interferes the ability of radar to detect and recognize sea surface targets, so the research on sea clutter is designed for radar system. Radar signal processing and ocean target detection and identification are of great significance. At present, radar sea surface target detection technology is widely used in marine safety, maritime rescue, monitoring of illegal smuggling and other fields. It is based on the above background that the research and analysis of the measured sea clutter data under the low grazing angle of high resolution radar is carried out. 1) firstly, the statistical characteristics of sea clutter amplitude are studied, and the fitting of the amplitude statistical model is carried out according to the measured sea clutter data of IPIX coherent radar, and the fitting effect is tested by goodness of fit. Secondly, the time correlation and de-correlation time between speckle component and Texture component in sea clutter time series are studied. 2) Hilbert-Huang transform is a new time-frequency analysis method proposed by Huang E, a Chinese American scientist in 1998, based on the signal itself. It mainly includes two processes: EMD empirical mode decomposition and Hilbert spectrum analysis. In this paper, the false modal function components produced in EMD decomposition are removed by correlation coefficient method. On the basis of this, a method for suppressing cross-interference terms of multi-component signal WVD transform based on EMD decomposition is proposed, and then the Hilbert-Huang transform is applied to the analysis and processing of the measured sea clutter data of IPIX coherent radar. The weak target detection under sea clutter background is realized from three different angles of low frequency IMF energy ratio, IMF mode energy entropy and ridge roughness of Hilbert spectrum. 3) based on the idea of time-Doppler velocity method and local amplitude statistics, the measured sea clutter data of IPIX coherent radar are studied and analyzed. The experimental results show that the two methods are effective. The location of the target distance unit can be detected without prior knowledge. 4) the real sea surface is complex and changeable, and the measured data often contain radar measurement noise and dynamic ambient noise from rough sea surface. In order to restrain the influence of these noises on the inherent characteristic analysis of sea clutter data, This paper focuses on the threshold denoising algorithm based on wavelet transform. The experimental simulation shows that the wavelet threshold method can be used to reduce the noise of the measured data. 5) aiming at the data processing of sea clutter measured by IPIX coherent radar, the control interface of Gui is designed by using MATLAB software, and various algorithms are embedded into it to realize man-machine interaction.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN959.72

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