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  本文選題：海雜波　切入點：運動目標檢測　出處：《西安電子科技大學》2015年碩士論文　論文類型：學位論文

【摘要】：海雜波背景下的動目標檢測尤其對弱小目標檢測的研究在軍用和民用領域都具有非常重要的意義,同時也是雷達目標檢測領域的一大難題。本文通過對海雜波背景下的目標檢測技術詳細研究,提出了一種基于載機相參雷達掃描回波數據進行多普勒補償的相參自適應最優(yōu)檢測方案。其主要優(yōu)勢是在進行空間多普勒補償后,將已檢測過的相鄰波位數據作為先驗信息,充分利用到當前分辨單元的目標檢測中。首先,分析了海雜波的物理組成機理和統(tǒng)計特性。在物理組成機理方面,主要介紹了海雜波的后向散射機理以及三分量模型;在統(tǒng)計特性方面,討論了海雜波的幅度特性和相關特性。海雜波特性的研究和建模,為目標檢測算法設計和性能提升提供了前提條件和理論支撐。其次,回顧了海雜波背景下的非相干累積檢測方法和自適應檢測方法,并分析了檢測方法的適用范圍以及其優(yōu)缺點。相干累積檢測方法主要包括均值類CFAR檢測算法、有序統(tǒng)計量類CFAR檢測算法和刪除單元平均的CFAR檢測算法;而自適應檢測方法介紹了廣義似然比檢測算法、自適應匹配濾波檢測算法和自適應歸一化匹配濾波檢測算法。本文在這些檢測算法的研究基礎上,進一步研究尋找應用范圍更為廣泛的檢測器。最后,為了整個空間多普勒補償的相參自適應最優(yōu)檢測方案的框架完整,介紹了海雜波的K-分布模型和α-AMF自適應檢測器。該檢測器是K-分布下廣泛適應于高斯雜波背景以及非高斯雜波背景下的近似最優(yōu)檢測器。由于自適應檢測器高度依賴于對雜波背景估計的協(xié)方差矩陣,給出了三種穩(wěn)健的雜波協(xié)方差矩陣估計方法。隨后,提出了基于機載波束掃描條件下空間多普勒補償的最優(yōu)自適應檢測方案。該方案在進行多普勒補償條件后,合理利用了已檢測過的相鄰波位參考單元回波數據,將之作為先驗信息,用于當前待檢測單元的目標檢測中。而且,為了消除雷達回波數據不同波位間由于載機運動引起的多普勒效應,提出了分辨單元載機多普勒補償量估計方法。仿真數據和實測海雜波數據實驗證實了提出方案的有效性并且與傳統(tǒng)只考慮距離維選取參考單元的檢測方案相比有明顯的性能改善。
[Abstract]:The research of moving target detection under sea clutter background, especially for small and weak target detection, is of great significance in both military and civil fields. At the same time, it is also a difficult problem in the field of radar target detection. In this paper, a coherent adaptive optimal detection scheme based on the scan echo data of aircraft coherent radar is proposed. The main advantage of the scheme is that after the spatial Doppler compensation, the detected adjacent wave position data are regarded as prior information. Firstly, the physical composition and statistical characteristics of sea clutter are analyzed. In terms of physical composition mechanism, the backscattering mechanism and three-component model of sea clutter are introduced. In terms of statistical characteristics, the amplitude and correlation characteristics of sea clutter are discussed. The research and modeling of sea clutter characteristics provide a prerequisite and theoretical support for the design of target detection algorithm and performance improvement. In this paper, the incoherent cumulant detection method and adaptive detection method in sea clutter background are reviewed, and the applicable range of the detection method and its advantages and disadvantages are analyzed. The coherent cumulative detection method mainly includes the mean class CFAR detection algorithm. The CFAR detection algorithm of ordered statistics and the CFAR detection algorithm of deleting unit average, while the generalized likelihood ratio detection algorithm is introduced in the adaptive detection method. Adaptive matched filter detection algorithm and adaptive normalized matched filter detection algorithm. Based on the research of these detection algorithms, this paper further studies the search for a more widely used detector. Finally, In order to complete the frame of the coherent adaptive optimal detection scheme for the whole spatial Doppler compensation, This paper introduces the K- distribution model of sea clutter and the 偽 -AMF adaptive detector, which is an approximate optimal detector for Gao Si clutter background and non-#china_person1# clutter background under K- distribution. The covariance matrix depends on the background estimation of clutter. Three robust estimation methods of clutter covariance matrix are presented. Then, an optimal adaptive detection scheme based on airborne beam scanning is proposed. The echo data of the detected adjacent wave position reference unit is reasonably used as a priori information for the target detection of the current detection unit. In order to eliminate the Doppler effect caused by the motion of the carrier between different wave levels of radar echo data, In this paper, a method of Doppler compensation estimation based on resolution unit is proposed. The simulation data and the measured sea clutter data show that the proposed scheme is effective and compared with the traditional detection scheme, which only takes into account the distance dimension of the reference unit. Significant performance improvements.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN959

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