本文選題：高分辨雷達(dá) + 擴(kuò)展目標(biāo)　； 參考：《國防科學(xué)技術(shù)大學(xué)》2014年博士論文

【摘要】：隨著雷達(dá)分辨率的提高,雷達(dá)目標(biāo)特性與雜波統(tǒng)計(jì)特性均發(fā)生了深刻的變化,這對雷達(dá)目標(biāo)檢測問題提出了更高的要求。本文采用理論分析與實(shí)驗(yàn)驗(yàn)證相結(jié)合的研究方法,針對高分辨條件下擴(kuò)展目標(biāo)檢測問題,系統(tǒng)研究了大拖尾雷達(dá)雜波模型及其參數(shù)估計(jì)、二維相關(guān)大拖尾分布雜波仿真以及基于一維距離像和SAR圖像的擴(kuò)展目標(biāo)檢測問題。第一章根據(jù)目前雷達(dá)技術(shù)發(fā)展的趨勢和實(shí)際應(yīng)用的需求,闡述了高分辨條件下擴(kuò)展目標(biāo)檢測的背景和意義,簡明扼要地總結(jié)了雷達(dá)雜波建模與仿真以及擴(kuò)展目標(biāo)檢測的研究現(xiàn)狀和發(fā)展趨勢,并對本文的主要工作進(jìn)行了概括。第二章研究了雷達(dá)雜波統(tǒng)計(jì)建模問題。首先對目前常見的雷達(dá)雜波模型進(jìn)行了綜述與分類,然后根據(jù)實(shí)際應(yīng)用的需求,總結(jié)了一個(gè)性能優(yōu)越的雜波模型應(yīng)具有的特點(diǎn),并通過分析對比,指出了基于乘積模型發(fā)展而來的KK分布與G0分布具有廣泛的應(yīng)用前景。第三章研究了大拖尾雜波模型的參數(shù)估計(jì)問題。首先綜述了經(jīng)典的參數(shù)估計(jì)方法,并通過理論分析與實(shí)驗(yàn)驗(yàn)證指出了各自的優(yōu)缺點(diǎn),然后提出了一種基于粒子群優(yōu)化的參數(shù)估計(jì)方法,該方法將雜波數(shù)據(jù)統(tǒng)計(jì)直方圖與雜波模型概率密度函數(shù)在部分采樣點(diǎn)上的差異作為代價(jià)函數(shù),通過粒子群優(yōu)化搜索參數(shù)的最優(yōu)值。然后分析了影響該方法參數(shù)估計(jì)精度的主要因素,最后通過仿真與實(shí)測雜波數(shù)據(jù)驗(yàn)證了該方法的優(yōu)越性。第四章研究了二維相關(guān)大拖尾雜波的模擬問題。首先提出了利用分段模擬的方法改善非線性變換的精度,以及具有任意相關(guān)函數(shù)的二維相關(guān)高斯雜波的生成方法,然后研究了基于MNLT的二維相關(guān)KK分布與G0分布雷達(dá)雜波的仿真方法;推導(dǎo)了KK分布與G0分布的SIRP特征概率密度函數(shù)表達(dá)式,研究了基于SIRP的二維相關(guān)KK分布與G0分布雷達(dá)雜波的仿真方法。仿真結(jié)果表明,兩種方法產(chǎn)生的二維相關(guān)大拖尾雷達(dá)雜波不論是幅度特性還是相關(guān)特性均滿足設(shè)定的要求。第五章研究了大拖尾雜波背景下基于一維距離像的擴(kuò)展目標(biāo)檢測問題。首先對檢測問題進(jìn)行了數(shù)學(xué)描述,闡述了一維擴(kuò)展目標(biāo)檢測需要解決的主要問題,然后基于KK分布與G0分布兩種大拖尾雜波模型提出了最優(yōu)檢測器、GLRT檢測器以及OS-GLRT檢測器等,并對這些檢測算法進(jìn)行了仿真與比較,指出OS-GLRT是一種比較實(shí)用的檢測器,并在不同的條件下對該檢測器進(jìn)行了性能分析。第六章研究了大拖尾雜波背景下基于SAR圖像的擴(kuò)展目標(biāo)檢測問題。首先對SAR圖像目標(biāo)檢測算法進(jìn)行了綜述與分類,然后重點(diǎn)分析了CFAR檢測算法的研究方向,提出了一種基于KK分布的全局CFAR檢測算法,并基于實(shí)測高分辨SAR圖像與基于G0分布的全局檢測算法以及基于自動篩選的CFAR檢測算法進(jìn)行了對比,說明了本文所提算法的有效性。第七章對全文工作進(jìn)行了系統(tǒng)地總結(jié),并給出了進(jìn)一步研究的方向和建議。
[Abstract]:With the enhancement of radar resolution, the radar target characteristics and the clutter statistical characteristics have undergone profound changes, which put forward higher requirements for the radar target detection problem. In this paper, a combination of theoretical analysis and experimental verification is adopted to study the large trailing radar in high resolution conditions. Wave model and its parameter estimation, two dimensional large trailing distribution clutter simulation and extended target detection based on one dimension range image and SAR image. The first chapter expounds the background and significance of the detection of extended target under high resolution conditions according to the current development trend of radar technology and the requirement of practical application. The research status and development trend of the modeling and Simulation of the clutter, and the development trend of the extended target detection are summarized. The second chapter studies the problem of the radar clutter statistical modeling. Firstly, the common radar clutter models are summarized and classified, and then a superior performance is summarized according to the practical application requirements. In the third chapter, the parameter estimation problem of the large trailing clutter model is studied in the third chapter. First, the classical parameter estimation method is summarized, and the theoretical analysis and experimental verification are presented. In this method, a parameter estimation method based on particle swarm optimization is proposed. This method uses the difference between the statistical histogram of the clutter data and the clutter model probability density function at the partial sampling point as the cost function, and searches the optimal value of the parameters by the particle swarm optimization. Then, the estimation accuracy of the parameter estimation is analyzed. The main factor is to verify the superiority of the method by simulation and measured clutter data. In the fourth chapter, the simulation of two dimensional large trailing clutter is studied. First, the accuracy of the nonlinear transformation is improved by the method of piecewise simulation, and the generation method of two-dimensional related Gauss clutter with arbitrary correlation function is studied. The simulation method of two dimensional correlation KK distribution and G0 distributed radar clutter based on MNLT is studied. The expression of the probability density function of the SIRP feature of the KK distribution and the G0 distribution is derived, and the simulation method of the two-dimensional correlation KK distribution based on SIRP and the clutter of the G0 distributed radar is studied. The simulation results show that the two two dimensional correlation large trailing radar is produced by the simulation results. The fifth chapter studies the problem of extended target detection based on one dimension distance image in the background of large trailing clutter. Firstly, the mathematical description of the detection problem is carried out, and the main problems to be solved in one dimension extended target detection are discussed, and then two kinds of KK distribution and G0 distribution are based on the problem. The large trailing clutter model proposed the optimal detector, the GLRT detector and the OS-GLRT detector, and simulated and compared these detection algorithms. It was pointed out that OS-GLRT was a more practical detector and analyzed the performance of the detector under different conditions. The sixth chapter studied the SAR image in the background of large trailing clutter. First, we summarize and classify the SAR image target detection algorithm, then focus on the research direction of the CFAR detection algorithm, and propose a global CFAR detection algorithm based on KK distribution, and based on the measured high resolution SAR image and the global detection algorithm based on G0 distribution and the CFAR based on the automatic screening of CFAR. The detection algorithm is compared, and the effectiveness of the proposed algorithm is illustrated. The seventh chapter systematically summarizes the full text work, and gives the direction and suggestions for further research.
【學(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN957.52

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本文編號：2099982