本文選題：低空風(fēng)切變 + 圖像處理��； 參考：《中國(guó)民航大學(xué)》2015年碩士論文

【摘要】：機(jī)場(chǎng)周圍低空風(fēng)切變嚴(yán)重影響空中交通效率,威脅空中交通的安全。低空風(fēng)切變的風(fēng)場(chǎng)具有多種表現(xiàn)形態(tài),其對(duì)飛行的影響相差甚遠(yuǎn),駕駛員需要參考其類型采取與之對(duì)應(yīng)的操作。研究低空風(fēng)切變類型判別課題對(duì)于航空航天來(lái)說(shuō)是十分必要的。本文在國(guó)內(nèi)外對(duì)低空風(fēng)切變研究的基礎(chǔ)上,選取低空側(cè)風(fēng)、低空逆風(fēng)、低空急流和下沖氣流四種典型的低空風(fēng)切變風(fēng)場(chǎng)進(jìn)行建模,從圖像多尺度分析的角度研究低空風(fēng)切變的類型識(shí)別方法。具體工作如下:首先,文中通過(guò)FLUENT的工程化建模方法構(gòu)建上述低空風(fēng)切變風(fēng)場(chǎng)的一般理想模型,采用基于激光雷達(dá)速度方位工作模式掃描風(fēng)場(chǎng)模型得到低空風(fēng)切變的PPI圖像,構(gòu)成激光雷達(dá)低空風(fēng)切變樣本庫(kù),庫(kù)中含有風(fēng)場(chǎng)徑向風(fēng)速信息完整和缺失的樣本圖像,為進(jìn)一步研究低空風(fēng)切變類型識(shí)別提供支持。其次,針對(duì)風(fēng)場(chǎng)樣本圖像切變線的各向異性,本文選擇了一種基于圖像多尺度分析理論的低空風(fēng)切變類型識(shí)別方法。統(tǒng)計(jì)每個(gè)L-NSCT變換尺度下的低空風(fēng)切變各子代的能量和標(biāo)準(zhǔn)差作為特征向量,得到對(duì)風(fēng)場(chǎng)旋轉(zhuǎn)不變的紋理特征。文中方法克服了傳統(tǒng)多尺度分析方法對(duì)圖像平移旋轉(zhuǎn)變化的敏感,提取到的特征能夠反映不同類型低空風(fēng)切變風(fēng)速的頻率分布。經(jīng)過(guò)支持向量機(jī)訓(xùn)練識(shí)別的結(jié)果表明,與目前的一些低空風(fēng)切變類型識(shí)別方法相比,本文采用的L-NSCT旋轉(zhuǎn)不變紋理特征提取算法提高了低空風(fēng)切變類型識(shí)別率。最后,考慮到頻率特征和空域特征均能表現(xiàn)低空風(fēng)切變的類型信息,在上文L-NSCT方法的基礎(chǔ)上,利用WLD獲取低空風(fēng)切變風(fēng)場(chǎng)的空域信息,選擇了一種基于特征融合的識(shí)別方法,獲得最終的低空風(fēng)切變紋理特征向量,送入SVM中訓(xùn)練識(shí)別,識(shí)別性能明顯高于現(xiàn)有的基于形狀特征的低空風(fēng)切變識(shí)別方法。
[Abstract]:The low-altitude wind shear around the airport seriously affects the air traffic efficiency and threatens the safety of air traffic. The wind field of low altitude wind shear has many forms and its influence on flight is very different. The pilot needs to refer to the type of wind shear and take the corresponding operation. It is necessary for aerospace to study the problem of low-altitude wind shear type discrimination. On the basis of the research on low level wind shear at home and abroad, four typical low level wind shear fields, namely, low level crosswind, low level headwind, low level jet flow and downwind flow, are selected to model the wind field in this paper. In this paper, the classification recognition method of low level wind shear is studied from the view of image multi-scale analysis. The specific work is as follows: firstly, the general ideal model of the wind shear wind field is constructed by the engineering modeling method of FLUENT, and the PPI image of the low altitude wind shear is obtained by scanning the wind field model based on the velocity azimuth mode of the lidar. A low altitude wind shear sample library of lidar is constructed, which contains complete and missing sample images of wind field radial wind speed information, which provides support for further research on low altitude wind shear type recognition. Secondly, in view of the anisotropy of wind field sample image shear line, a low level wind shear type recognition method based on image multi-scale analysis theory is selected in this paper. The energy and standard deviation of each generation of low-altitude wind shear at the scale of L-NSCT transform are calculated as feature vectors, and the texture features with invariable rotation to wind field are obtained. The method overcomes the sensitivity of traditional multi-scale analysis method to image translation and rotation, and the extracted features can reflect the frequency distribution of different types of low-altitude wind shear wind speed. The results of SVM training and recognition show that the L-NSCT rotation invariant texture feature extraction algorithm proposed in this paper improves the recognition rate of low-altitude wind shear type compared with some current low-altitude wind shear type recognition methods. Finally, considering that the frequency feature and the spatial feature can represent the type information of the low-altitude wind shear, on the basis of the L-NSCT method above, using WLD to obtain the spatial information of the low-altitude wind shear field, a recognition method based on feature fusion is selected. Finally, the final low-altitude wind shear texture feature vector is obtained, and then trained in SVM. The recognition performance is obviously higher than the existing low-altitude wind shear recognition method based on shape features.
【學(xué)位授予單位】：中國(guó)民航大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：V321.225;TP391.41

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