本文選題：目標(biāo)檢測 + 聯(lián)合特征　； 參考：《南京信息工程大學(xué)》2014年碩士論文

【摘要】：海雜波是雷達(dá)接收白海表面的散射回波,它在很大程度上影響了雷達(dá)對冰山、油輪和其他海上目標(biāo)的定位和檢測。因此,研究海雜波特性對減輕散射的影響、提高海雜波中目標(biāo)檢測是非常關(guān)鍵的。分形是非線性科學(xué)的重要分支,在處理非線性、非平穩(wěn)信號方面發(fā)揮了很好的優(yōu)勢,尤其是在雷達(dá)雜波的建模和目標(biāo)檢測方面得到了很好的發(fā)展與應(yīng)用。本文重在研究分形理論在海雜波和微弱目標(biāo)檢測領(lǐng)域的應(yīng)用,理論研究和實(shí)測數(shù)據(jù)驗(yàn)證相結(jié)合,分析海雜波形成的基本機(jī)理和分形理論對于海上微弱目標(biāo)檢測的優(yōu)勢,結(jié)合IPIX實(shí)測雷達(dá)數(shù)據(jù)來檢驗(yàn)所提檢測方法的有效性。具體的研究內(nèi)容如下： 介紹了分形及其相關(guān)理論,尤其是分形維數(shù)和多重分形譜的定義和求解方法,分析了分形及其相關(guān)理論在雷達(dá)目標(biāo)檢測領(lǐng)域的應(yīng)用,應(yīng)用單一和多重分形方法分別對實(shí)測海雜波數(shù)據(jù)的分形特征進(jìn)行仿真分析。 在雷達(dá)回波中提取海雜波的分形參數(shù)和功率譜熵特征組成二維向量,運(yùn)用凸包訓(xùn)練算法得到純海雜波分類區(qū)域,同樣對未知待測海雜波提取這兩個(gè)特征參量,以此特征參量所對應(yīng)的點(diǎn)是否在此分類區(qū)域內(nèi)判別是否存在目標(biāo)。利用IPIX雷達(dá)數(shù)據(jù),證明了所提算法優(yōu)于用單個(gè)特征差異作為統(tǒng)計(jì)量的方法,在相同虛警概率下檢測效果明顯提高,為雷達(dá)目標(biāo)檢測提供了新的檢測方案。 在實(shí)測數(shù)據(jù)的基礎(chǔ)上,分析海雜波和目標(biāo)回波的多重分形譜及廣義Hurst指數(shù),多組數(shù)據(jù)實(shí)驗(yàn)發(fā)現(xiàn)純海雜波和目標(biāo)回波的廣義Hurst指數(shù)曲線分離度較大,尤其是在低尺度條件下,而不同距離單元的純海雜波間廣義Hurst指數(shù)曲線靠的很近�；谶@些發(fā)現(xiàn),我們選取了三個(gè)多重分形特征參數(shù)來量化兩者之間的差異,利用SVM分類器對目標(biāo)回波和純海雜波進(jìn)行分類實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明：無論是對于訓(xùn)練樣本還是測試樣本,基于多重分形特征的目標(biāo)檢測對目標(biāo)單元和純海雜波單元的正確檢測率都在90%以上,同時(shí),指出不同極化方式下分類準(zhǔn)確率存在一定差異,說明了采用多重分形特征參數(shù)進(jìn)行檢測能基本實(shí)現(xiàn)了目標(biāo)單元和純海雜波單元的分離,具有一定的實(shí)用價(jià)值。
[Abstract]:Sea clutter is a radar that receives scattering echoes from the white sea surface. To a great extent, it affects radar location and detection of icebergs, tankers and other offshore targets. Therefore, it is very important to study the characteristics of sea clutter to reduce the scattering and improve the target detection in sea clutter. Fractal is an important branch of nonlinear science, which has played a good role in dealing with nonlinear and non-stationary signals, especially in radar clutter modeling and target detection. This paper focuses on the application of fractal theory in the field of sea clutter and weak target detection. The basic mechanism of sea clutter formation and the advantage of fractal theory for weak target detection at sea are analyzed by combining theoretical research with the verification of measured data. The effectiveness of the proposed detection method is verified by using IPIX radar data. The specific contents of the study are as follows: This paper introduces the fractal theory, especially the definition and solution of fractal dimension and multifractal spectrum, and analyzes the application of fractal theory in radar target detection. The fractal characteristics of measured sea clutter data are simulated and analyzed by single and multifractal methods. The fractal parameters and power spectral entropy features of sea clutter are extracted from radar echo to form a two-dimensional vector. The classification region of pure sea clutter is obtained by using convex hull training algorithm. The two characteristic parameters are also extracted for unknown sea clutter. Whether the points corresponding to this characteristic parameter exist in this classification area. Using IPIX radar data, it is proved that the proposed algorithm is superior to the method using single feature difference as statistic, and the detection effect is improved obviously under the same false alarm probability, which provides a new detection scheme for radar target detection. Based on the measured data, the multifractal spectrum and generalized Hurst exponent of sea clutter and target echo are analyzed. It is found that the generalized Hurst exponent curve of pure sea clutter and target echo is more separated, especially under the condition of low scale. The generalized Hurst exponent curves of pure sea clutter with different distances are very close. Based on these findings, we select three multifractal feature parameters to quantify the difference between them, and use SVM classifier to classify target echo and pure sea clutter. The experimental results show that the correct detection rate of target unit and pure sea clutter unit based on multifractal feature is more than 90% for both training samples and test samples. It is pointed out that there are some differences in classification accuracy under different polarization modes, which indicates that the detection of multifractal feature parameters can basically realize the separation of target units from pure sea clutter units, which is of certain practical value.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：P715.9

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