【摘要】：隨著距離分辨率的提高,目標(biāo)能量分布在雷達(dá)回波中的多個距離單元內(nèi),被稱作距離擴展目標(biāo)或者分布式目標(biāo)。距離擴展目標(biāo)的回波之中包含目標(biāo)更多的信息,如何有效地利用這些信息成為雷達(dá)技術(shù)領(lǐng)域迫切需要解決的問題。目前針對雷達(dá)高分辨距離像的目標(biāo)識別問題已經(jīng)得到了廣泛的關(guān)注,但對于距離擴展目標(biāo)的檢測更多地停留在理論研究層面,如何對檢測結(jié)果進行鑒別,有效地去除檢測結(jié)果中虛警的研究工作則很少涉及。本文對距離擴展目標(biāo)的檢測方法進行研究,并將目標(biāo)的檢測結(jié)果和一類分類器相結(jié)合,提出了一種基于一維距離像的目標(biāo)鑒別方法。本文的主要工作如下:1.對線性調(diào)頻步進信號和雷達(dá)雜波統(tǒng)計模型進行研究。給出了線性調(diào)頻步進信號的參數(shù)選取準(zhǔn)則、速度補償方法和高分辨距離像合成方法,并結(jié)合仿真結(jié)果進行比較。然后介紹了雷達(dá)雜波數(shù)據(jù)的功率譜模型和幅度的統(tǒng)計模型,給出了兩種比較常用的雜波模擬方法:零記憶非線性變化法和球不變隨機過程法。2.對距離擴展目標(biāo)的檢測問題進行研究。介紹了統(tǒng)計信號的檢測理論,給出了奈曼-皮爾遜準(zhǔn)則和恒虛警率檢測的含義。接著給出了幾種針對點目標(biāo)的檢測方法和對應(yīng)的檢測門限的計算方法,并結(jié)合仿真結(jié)果說明計算得到的檢測門限可以保證虛警概率的近似不變。然后介紹了基于二進制積累的檢測方法,提出了瑞利雜波模型和對數(shù)正態(tài)雜波模型下距離擴展目標(biāo)的直接積累檢測法,并給出相應(yīng)的檢測統(tǒng)計量與檢測門限的計算方法。最后通過仿真實驗對幾種檢測方法進行比較。3.針對一維距離像的目標(biāo)鑒別問題進行研究。首先,結(jié)合實例對最近鄰一類分類器、K-近鄰一類分類器以及K-中心一類分類器的主要思想和分類過程進行說明。接著引入衡量兩個點集間差異性大小的Hausdorff距離,對最近鄰一類分類器進行改進,結(jié)合目標(biāo)檢測的結(jié)果提出了一種針對一維距離像的目標(biāo)鑒別方法,并將該鑒別方法推廣到K-中心一類分類器。然后基于實測數(shù)據(jù),詳細(xì)分析了一類分類器的不同參數(shù)對鑒別性能的影響,并比較了歐氏距離和Hausdorff距離的鑒別性能,驗證了提出的鑒別方法的有效性,結(jié)合實驗比較了改進的最近鄰一類分類器和K-中心一類分類器在鑒別過程中各自的優(yōu)勢。
[Abstract]:With the improvement of range resolution, target energy is distributed in multiple range units in radar echo, which is called range extended target or distributed target. The echo of the extended range target contains more information of the target. How to utilize this information effectively has become an urgent problem in the field of radar technology. At present, the problem of target recognition of radar high resolution range profile has been paid more attention to, but the detection of extended range target is more focused on the theoretical research, how to identify the detection results. The research work of removing false alarm effectively is seldom involved. In this paper, the detection method of extended range target is studied, and a target discrimination method based on one-dimensional range profile is proposed by combining the detection results of target with a class of classifiers. The main work of this paper is as follows: 1. The statistical model of linear frequency modulation step signal and radar clutter is studied. The parameter selection criterion, velocity compensation method and high resolution range profile synthesis method of LFM stepper signal are presented, and the simulation results are compared. Then, the power spectrum model and amplitude statistical model of radar clutter data are introduced, and two common clutter simulation methods are given: zero memory nonlinear variation method and spherical invariant random process method. The detection of extended range targets is studied. This paper introduces the detection theory of statistical signal, and gives the meaning of Neiman-Pearson criterion and CFAR detection. Then several detection methods for point targets and the corresponding detection threshold calculation methods are given, and the simulation results show that the calculated detection threshold can guarantee the approximate invariance of false alarm probability. Then the detection method based on binary accumulation is introduced, and the direct accumulation detection method for extended distance target under Rayleigh clutter model and logarithmic normal clutter model is proposed, and the corresponding detection statistics and detection threshold are calculated. Finally, several detection methods are compared by simulation experiments. 3. 3. The target identification problem of one-dimensional range profile is studied. Firstly, the main ideas and classification processes of the nearest neighbor class classifier and the K- center class classifier are explained by an example. Then the Hausdorff distance is introduced to measure the difference between the two sets of points, and the nearest neighbor classifier is improved, and a target discriminating method for one dimensional range profile is proposed based on the result of target detection. The discriminant method is extended to K-center classifier. Then, based on the measured data, the effects of different parameters of a class of classifiers on the discriminant performance are analyzed in detail, and Euclidean distance and Hausdorff distance are compared to verify the effectiveness of the proposed discriminant method. The advantages of the improved nearest neighbor classifier and the K- center classifier in the discriminant process are compared with experiments.
【學(xué)位授予單位】：西安電子科技大學(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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相關(guān)期刊論文 前1條

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本文編號：2228083