發(fā)布時間：2018-03-31 01:19

  本文選題：一維距離像　切入點：平移不變特征　出處：《沈陽理工大學(xué)》2015年碩士論文

【摘要】：利用高分辨雷達對目標進行識別是當代雷達系統(tǒng)的一個主要發(fā)展趨勢。目前基于高分辨雷達的目標識別在軍事及民用方面都已經(jīng)有了一定程度的應(yīng)用。本論文利用實測高分辨雷達回波數(shù)據(jù)進行實驗,重點研究了雷達識別系統(tǒng)中目標的特征提取與融合,并利用支持向量機分類器對選定的特征進行識別。主要工作包含以下內(nèi)容:首先,在針對雷達目標一維距離像的形成和性質(zhì)進行研究的基礎(chǔ)上,發(fā)現(xiàn)雷達目標的一維距離像中包含了大量的目標結(jié)構(gòu)以及形狀等信息,因此,針對一維距離像進行特征的提取。在研究了一維距離像平移敏感性的基礎(chǔ)上,采用提取平移不變特征的方法來克服這一問題。分別提取了目標的功率譜特征、中心距特征以及幅度譜差分特征,并且研究發(fā)現(xiàn)了目標的這三種提取特征具有差異性。其次,為了獲取有效特征,本文提出了一種基于粗糙集改進的主成分分析融合方法應(yīng)用于雷達目標識別上。在雷達目標特征識別系統(tǒng)中最重要的環(huán)節(jié)就是目標的特征選取。選取的特征既要能夠描述目標,又要與其他相似目標有一定的差異性。在不影響特征信息含量的同時還應(yīng)該盡量的減少特征維數(shù),爭取利用最少的特征來包含目標最有效的信息,進而做到快速、高效、準確的識別目標。對目標的功率譜特征、中心距特征以及幅度譜差分特征進行主成分分析,然后基于粗糙集理論對目標特征進行屬性約簡,使得融合后的特征具有大量的目標信息,同時大幅度地降低了特征維數(shù),從而保證該融合特征的優(yōu)越性。最后,詳細的介紹了支持向量機分類器的原理及應(yīng)用,并應(yīng)用三種不同的算法配合多種核函數(shù)將一維距離像的目標融合特征和單一特征分別進行識別,研究結(jié)果表明,基于粗糙集改進的主成分分析融合的特征不僅在識別方面強于其他特征,而且其特征維數(shù)也大幅度的降低了。這樣既提高了識別系統(tǒng)的識別率,同時也節(jié)省了識別系統(tǒng)的存儲空間,減輕了系統(tǒng)的運算復(fù)雜程度。
[Abstract]:Target recognition based on high resolution radar is a main developing trend of modern radar system. At present, target recognition based on high resolution radar has been applied in military and civilian fields to a certain extent. The experiment is carried out with the measured high resolution radar echo data. The feature extraction and fusion of targets in radar recognition system are studied emphatically, and the selected features are recognized by support vector machine classifier. The main work includes the following: first, Based on the research on the formation and properties of radar target one-dimensional range profile, it is found that the one-dimensional range profile of radar target contains a lot of information such as the structure and shape of the target. Based on the study of the translation sensitivity of the one-dimensional range profile, the method of extracting the translation invariant feature is used to overcome this problem. The power spectrum features of the target are extracted, respectively. The center distance feature and amplitude spectrum difference feature, and the study found that the three extraction features of the target have differences. Secondly, in order to obtain effective features, In this paper, an improved principal component analysis fusion method based on rough set is proposed for radar target recognition. The most important part of radar target feature recognition system is the feature selection of the target. The selected feature must be able to describe the target. We should not affect the content of feature information, but also try to reduce the dimension of the feature, try to use the least features to include the most effective information of the target, so as to be fast and efficient. The power spectrum feature, center distance feature and amplitude spectrum difference feature of the target are analyzed by principal component analysis, and then the target feature is reduced based on rough set theory. The fusion features have a large amount of target information and greatly reduce the feature dimension, so as to ensure the superiority of the fusion feature. Finally, the principle and application of SVM classifier are introduced in detail. Three different algorithms combined with a variety of kernel functions are used to identify the target fusion feature and the single feature of the one-dimensional range profile, and the results show that, The feature of principal component analysis fusion based on rough set is not only stronger than other features in recognition, but also its feature dimension is greatly reduced, which not only improves the recognition rate of the recognition system, but also improves the recognition rate of the recognition system. At the same time, the storage space of the recognition system is saved, and the complexity of the system is reduced.
【學(xué)位授予單位】：沈陽理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN957.52

【參考文獻】

相關(guān)碩士學(xué)位論文 前1條

1 李永勝;某艦載雷達力學(xué)性能研究[D];南京理工大學(xué);2011年

，

本文編號：1688516