本文選題：海雜波 + 目標(biāo)檢測��； 參考：《西安電子科技大學(xué)》2016年博士論文

【摘要】：海面監(jiān)視雷達(dá)可以完成對大范圍海面的預(yù)警、監(jiān)視和探測,在軍事和民用領(lǐng)域均有廣泛應(yīng)用。由于高分辨海雜波的空時(shí)非平穩(wěn)特性,傳統(tǒng)目標(biāo)檢測方法面臨低檢測概率,高虛警的問題,使得海雜波背景下的對海面目標(biāo)特別是慢速、漂浮小目標(biāo)的檢測成為國內(nèi)外專家和學(xué)者研究的重點(diǎn)與難點(diǎn)。本文在對實(shí)測雷達(dá)海雜波數(shù)據(jù)特性分析的基礎(chǔ)上,主要研究在高分辨海雜波背景下海面慢速、漂浮小目標(biāo)的檢測問題。研究的問題包括：海雜波分形特性分析與目標(biāo)檢測方法、基于快速凸包學(xué)習(xí)的三特征聯(lián)合檢測方法、匹配與高分辨海雜波多普勒譜特性的檢測方法以及基于塊白化海雜波抑制的海雜波時(shí)頻特性分析與目標(biāo)檢測方法。本文主要研究成果概括如下：1.分析了海雜波幅度時(shí)間序列的多尺度分形特性與分形特征的空時(shí)變性。在對海雜波擴(kuò)展自相似建模的基礎(chǔ)上,利用多尺度Hurst指數(shù)討論了在三個(gè)尺度區(qū)間內(nèi)海雜波時(shí)間序列的起伏的主導(dǎo)因素,定性的分析了噪聲對序列分形特性的影響；通過對不同時(shí)間不同海況條件下采集的實(shí)測數(shù)據(jù)分析發(fā)現(xiàn)海雜波的分形特征如Hurst指數(shù)隨外界條件如海態(tài)、雷達(dá)照射方向與浪向的夾角的不同而改變。提出了兩個(gè)基于改進(jìn)分形特征的海面漂浮小目標(biāo)檢測方法。實(shí)測數(shù)據(jù)表明,由于利用了海雜波序列的多尺度性和空時(shí)變性,基于多尺度Hurst指數(shù)和基于相對Hurst指數(shù)相比基于Hurst指數(shù)的方法有更好的檢測性能。2.討論了傳統(tǒng)目標(biāo)檢測問題與異常檢測中單分類問題的聯(lián)系,為海面目標(biāo)檢測問題提供了新的解決途徑。由于海面目標(biāo)的復(fù)雜性和多樣性,通常無法獲取所有種類目標(biāo)回波,因此我們將純雜波回波看作正常觀測,含目標(biāo)回波看作異常觀測,并從回波中提取三個(gè)在兩種觀測模式下具有明顯差異的特征,分析了兩種模式下特征向量在三維特征空間上的可分性。提出了利用正常觀測訓(xùn)練回波樣本,通過快速凸包學(xué)習(xí)算法在特征空間獲得檢測判決區(qū)域的非參數(shù)方法,并提出了三特征聯(lián)合檢測方法。與基于海雜波分形特性的目標(biāo)檢測方法相比,該方法可以在短的觀測時(shí)間獲得較好的檢測結(jié)果。3.在實(shí)測海雜波數(shù)據(jù)基礎(chǔ)上,對海雜波多普勒功率譜建模。與地雜波相比,海雜波具有較寬的多普勒帶寬。我們在多普勒域?qū)⒑ｋs波功率譜分為雜波占優(yōu)單元、噪聲占優(yōu)單元以及雜波噪聲混合單元,并將海雜波多普勒譜建模為一個(gè)正隨機(jī)過程,該隨機(jī)過程在每個(gè)多普勒單元滿足具有不同形狀參數(shù)和尺度參數(shù)的K分布模型�？紤]到海面漂浮目標(biāo)在多普勒域的能量擴(kuò)散現(xiàn)象,提出了匹配于海雜波多普勒譜特性的雙重檢測方法。實(shí)測數(shù)據(jù)的實(shí)驗(yàn)表明當(dāng)信雜比較高時(shí)該檢測器具有很好的檢測性能。4.高分辨海雜波的非平穩(wěn)性導(dǎo)致在較長觀測時(shí)間下,傳統(tǒng)雜波白化方法在估計(jì)雜波協(xié)方差矩陣時(shí)無法得到足夠的參考單元回波樣本,降低了雜波抑制的有效性,進(jìn)而限制了檢測器在長時(shí)間積累條件下的目標(biāo)檢測性能。針對這一問題,我們提出了塊白化的海雜波抑制方法,將海雜波背景下的檢測問題轉(zhuǎn)化為在近似白噪聲背景下的檢測問題,有效抑制時(shí)頻平面上交叉項(xiàng)對回波能量累積的影響,在此基礎(chǔ)上給出了：(1)基于時(shí)頻脊引導(dǎo)的Hough變換的目標(biāo)檢測方法。提出的時(shí)頻脊引導(dǎo)的Hough具有比傳統(tǒng)Hough變換更小的計(jì)算復(fù)雜度,并能較好的積累目標(biāo)能量,具有較強(qiáng)的實(shí)際應(yīng)用價(jià)值；(2)基于改進(jìn)凸包學(xué)習(xí)算法的時(shí)頻雙特征檢測方法。利用特征分布的先驗(yàn)知識,改進(jìn)的凸包學(xué)習(xí)算法可以更高效的獲得檢測判決區(qū)域。針對純雜波回波和目標(biāo)所在單元回波在時(shí)頻平面上的時(shí)頻脊的差異,給出了兩種提取脊能量和脊全變差的方法,并用Bhattacharyya距離定量的評估了兩種提取方法提取的兩種模式下的特征在特征平面上的可分性。兩個(gè)特征對于檢測海面漂浮目標(biāo)來說具有較好的互補(bǔ)性,通過實(shí)測數(shù)據(jù)驗(yàn)證,得到的雙特征檢測器具有很好的檢測性能。
[Abstract]:Sea surface surveillance radar can achieve early warning, monitoring and detection of large scale sea surface. It is widely used in military and civil fields. Due to the non-stationary characteristics of high resolution sea clutter, the traditional target detection method faces the problem of low detection probability and high false alarm, which makes the sea clutter background to the sea target especially slow and floating. The detection of target has become the focus and difficulty of experts and scholars at home and abroad. On the basis of the analysis of the data characteristics of the measured radar sea clutter, this paper mainly studies the problem of low speed and floating small target detection in the background of high resolution sea clutter. The research problems include the fractal characteristic analysis and target detection method of sea clutter, based on the analysis of sea clutter and the method of target detection. The three feature joint detection method for fast convex hull learning, the detection method of matching and high resolution sea clutter Doppler spectrum characteristics and the time-frequency characteristic analysis and target detection method of sea clutter suppression based on the block white sea clutter suppression. The main research results are summarized as follows: 1. the multi-scale fractal characteristics of the sea clutter amplitude time series are analyzed. On the basis of the self similar modeling of sea clutter expansion, the dominating factors of the fluctuation of sea clutter time series in three scales are discussed on the basis of the self similar modeling of the sea clutter expansion. The influence of noise on the fractal characteristics of the sequence is qualitatively analyzed, and the measured data collected at different time and different sea conditions are measured. The data analysis shows that the fractal characteristics of the sea clutter, such as the Hurst exponent vary with the external conditions such as the sea state, the direction of the radar and the angle of the wave direction, are changed. Two methods for detecting the floating small targets on the sea surface are proposed based on the improved fractal features. The measured data show that the multiscale and space-time variability of the sea clutter sequence is based on the multiscale and space-time variability of the sea clutter sequence. The multiscale Hurst index and the method based on the relative Hurst index based on the Hurst index have better detection performance.2.. The relationship between the traditional target detection problem and the single classification problem in the anomaly detection is discussed, which provides a new solution for the problem of the sea surface target detection. For all kinds of target echoes, we regard pure clutter echoes as normal observations, which include target echoes as abnormal observations, and extract three features that have distinct differences in the two modes of observation from the echoes, and analyze the separability of the eigenvectors under the two modes in the three-dimensional feature space. The sample, using the fast convex hull learning algorithm to obtain the non parametric method of detecting the decision area in the feature space, and proposes a joint detection method of three features. Compared with the target detection method based on the fractal characteristic of the sea clutter, the method can obtain better detection results at short observation time, on the basis of the measured sea clutter data, the.3. is on the sea. Clutter Doppler power spectrum modeling. Compared with ground clutter, sea clutter has a wider Doppler bandwidth. In Doppler domain, we divide the sea clutter power spectrum into clutter dominant unit, noise dominant unit and mixed wave noise mixed unit, and model sea clutter Doppler spectrum as a positive random process. This random process is in each Doppler. The unit satisfies the K distribution model with different shape and scale parameters. Considering the energy diffusion of the floating target in the Doppler domain, a dual detection method matching the Doppler spectrum characteristics of the Yu Hai clutter is proposed. The experimental data show that the detector has a good detection performance.4. high resolution when the signal is high. The non stationarity of the sea clutter leads to the fact that the traditional clutter whitening method can not get enough reference unit echo samples when estimating the covariance matrix of the clutter in the long time of observation, which reduces the effectiveness of the clutter suppression and limits the detection performance of the detector under the condition of long time accumulation. The method of block whitening sea clutter suppression, the detection problem under the background of the sea clutter background is transformed into a detection problem under the approximate white noise background, and the effect of the cross term on the echo energy accumulation on the time frequency plane is effectively suppressed. On this basis, the target detection method based on the Hough transform based on the time frequency ridge guidance is given. The time frequency ridge is proposed. The Hough has a smaller computational complexity than the traditional Hough transform, and it can accumulate the target energy well, and has a strong practical application value. (2) a time-frequency dual feature detection method based on the improved convex packet learning algorithm. Using the prior knowledge of the feature distribution, the improved convex packet learning algorithm can get the detection area more efficiently. In view of the difference in the time frequency ridges of the pure clutter echo and the target unit echo on the time frequency plane, two methods of extracting ridge energy and ridge total variation are given, and the Bhattacharyya distance is used to quantitatively evaluate the separability of the characteristics on the characteristic flat surface of the two modes extracted by the two extraction methods. The two features are for the detection of the sea surface. The results show that the dual feature detector has good detection performance.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TN957.52

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 吳曼青;;數(shù)字陣列雷達(dá)的發(fā)展與構(gòu)想[J];雷達(dá)科學(xué)與技術(shù);2008年06期

，

本文編號：2018210