本文選題：空時(shí)自適應(yīng)處理　切入點(diǎn)：協(xié)方差矩陣估計(jì)　出處：《國防科學(xué)技術(shù)大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：空時(shí)自適應(yīng)處理(Space-Time Adaptive Processing,STAP)技術(shù)由于能夠有效提高機(jī)載雷達(dá)的雜波抑制性能和目標(biāo)檢測(cè)性能而受到了廣泛關(guān)注。STAP中一個(gè)關(guān)鍵的步驟是估計(jì)待檢測(cè)單元的雜波協(xié)方差矩陣(Clutter Covariance Matrix,CCM)。CCM的估計(jì)方法有兩類,一類是利用雜波的統(tǒng)計(jì)特性,借助于和待檢測(cè)單元滿足獨(dú)立同分布的訓(xùn)練樣本,根據(jù)一定的準(zhǔn)則(如最大似然準(zhǔn)則)實(shí)現(xiàn)估計(jì);另一類是利用雜波的結(jié)構(gòu)特性,借助于雜波模型,通過估計(jì)雜波模型中的參數(shù)實(shí)現(xiàn)估計(jì)。第一類方法在均勻樣本數(shù)目足夠多的情況下能實(shí)現(xiàn)比較好的估計(jì)。然而,實(shí)際環(huán)境中樣本往往是非均勻的,直接利用非均勻的樣本數(shù)據(jù)來估計(jì)協(xié)方差矩陣會(huì)引起估計(jì)誤差,導(dǎo)致性能的下降。第二類方法在模型與真實(shí)協(xié)方差矩陣匹配且各參數(shù)估計(jì)準(zhǔn)確的情況下能取得比較好的估計(jì)性能,然而計(jì)算量較大。研究人員發(fā)現(xiàn)發(fā)掘并使用先驗(yàn)知識(shí)實(shí)現(xiàn)智能化信號(hào)處理能有效提高雜波抑制的性能。本文在此背景下圍繞STAP中基于知識(shí)的雜波協(xié)方差矩陣估計(jì)方法展開研究工作。第二章重點(diǎn)討論了CCM的特性,包括特征譜、功率譜以及實(shí)際因素對(duì)CCM的影響。仿真結(jié)果表明通道誤差、雜波起伏以及載機(jī)偏航等實(shí)際因素會(huì)引起雜波自由度的增加和功率譜的展寬或變形。第三章提出了一種新的基于幾何特性(協(xié)方差矩陣之間的距離)選擇訓(xùn)練樣本的方法。文章中分析了多種距離指標(biāo)(包括歐式距離,黎曼距離,譜距離,物理歐式距離以及物理譜距離),并討論了三種計(jì)算距離的方法(相鄰樣本協(xié)方差矩陣之間的距離,樣本協(xié)方差矩陣與采樣協(xié)方差矩陣之間的距離以及樣本協(xié)方差矩陣與知識(shí)輔助的協(xié)方差矩陣之間的距離)。仿真結(jié)果表明利用樣本協(xié)方差矩陣與知識(shí)輔助的協(xié)方差矩陣之間的黎曼距離、物理歐式距離或物理譜距離能更加有效地實(shí)現(xiàn)樣本的選擇。第四章研究了基于先驗(yàn)合成孔徑雷達(dá)(Synthetic Aperture Radar,SAR)圖像的CCM估計(jì)方法。忽略散射體方位散射特性的變化,理想情況下,基于SAR圖像的協(xié)方差矩陣估計(jì)誤差較小,能獲得比較好的檢測(cè)性能。然而,一些強(qiáng)雜波點(diǎn)(如高壓電線,橋,房屋等)的散射特性隨方位視角變化很大。此外,由于天氣、氣候的影響,SAR圖像和雷達(dá)探測(cè)的場(chǎng)景散射特性可能不一致,這些實(shí)際因素使得利用SAR圖像估計(jì)得到的協(xié)方差矩陣存在誤差。針對(duì)這兩種情況,文章分別提出了利用子孔徑SAR圖像獲取某個(gè)特定角度雜波單元散射特性的方法以及聯(lián)合使用SAR圖像和訓(xùn)練樣本進(jìn)行有色加載的方法,仿真結(jié)果驗(yàn)證了方法的有效性。
[Abstract]:Space-Time Adaptive processing (Space-Time Adaptive processing) technology has attracted wide attention for its ability to effectively improve the clutter suppression performance and target detection performance of airborne radar. One of the key steps in STAP is to estimate the clutter covariance moments of the unit to be detected. There are two methods for estimating the Clutter Covariance Matrix. CCM. One is to use the statistical characteristics of clutter, and the other is to use the structural characteristics of clutter to realize the estimation according to certain criteria (such as maximum likelihood criterion), with the help of training samples which satisfy the independent and same distribution with the units to be detected. With the aid of clutter model, the parameters of clutter model can be estimated by estimating the parameters of the clutter model. The first kind of method can achieve better estimation when the number of uniform samples is large enough. However, the samples are often non-uniform in real environment. Direct use of non-uniform sample data to estimate the covariance matrix will lead to estimation errors. The second method can obtain better estimation performance when the model matches the real covariance matrix and the parameters are estimated accurately. The researchers found that intelligent signal processing with prior knowledge can effectively improve the performance of clutter suppression. In this paper, the clutter covariance matrix estimation based on knowledge in STAP is proposed. Methods in the second chapter, the characteristics of CCM are discussed. The effects of characteristic spectrum, power spectrum and actual factors on CCM are included. The simulation results show that the channel error, Clutter fluctuation and carrier yaw will cause the increase of clutter degree of freedom and the broadening or distortion of power spectrum. In chapter 3, a new training sample based on geometric characteristics (distance between covariance matrices) is proposed. This paper analyzes a variety of distance indicators (including Euclidean distance, Euclidean distance, Euclidean distance, Euclidean distance, Riemannian distance, spectral distance, physical Euclidean distance and physical spectral distance are discussed. The distance between the sample covariance matrix and the sample covariance matrix and the distance between the sample covariance matrix and the knowledge-assisted covariance matrix. The simulation results show that the sample covariance matrix and the knowledge-assisted covariance matrix are used. Riemann distance between matrices, Physical Euclidean distance or physical spectral distance can be used to select samples more effectively. Chapter 4th studies the method of CCM estimation based on a priori synthetic Aperture radar (SAR) image. The estimation error of covariance matrix based on SAR image is small and can obtain better detection performance. However, the scattering characteristics of some strong clutter points (such as high-voltage wire, bridge, house, etc.) vary greatly with the azimuth angle of view. The effects of climate on the scattering characteristics of SAR images and radar detection scenes may be inconsistent. These practical factors cause errors in the covariance matrix estimated from SAR images. In this paper, a subaperture SAR image is proposed to obtain the scattering characteristics of a particular angle clutter unit, and a method to combine SAR images and training samples for colored loading is proposed. The simulation results show that the method is effective.
【學(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN957.52

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