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  本文選題：雷達目標識別 + 稀疏學習�。� 參考：《南京航空航天大學》2016年碩士論文

【摘要】：雷達目標識別技術是基于雷達回波信號,提取與目標特性相關的信息,實現(xiàn)目標屬性或類別的判定。隨著國際形勢的發(fā)展,雷達目標識別越來越受到世界各國科研人員的青睞。隨著高分辨率雷達體制的應用,使得獲得更為細致的目標幾何結構信息和細節(jié)信息成為可能,而高分辨率雷達(High Resolution Radar,HRR)和合成孔徑雷達(Synthetic Aperture Radar,SAR)兩種體制的雷達的回波信號HRRP和SAR圖像,作為典型的高分辨率雷達信號,也成為當前各國雷達目標識別研究的熱點。本文在稀疏學習理論基礎上,研究基于HRRP目標和SAR圖像目標的雷達目標識別方法,主要的研究工作如下:1.研究了稀疏學習理論。首先,對三種典型的稀疏建模方式、三類經典的稀疏求解方法、以及稀疏學習的應用進行了闡述;其次,分別研究了HRRP目標和SAR圖像常用的稀疏表示方法,并對其稀疏性進行了分析。2.提出了一種基于貝葉斯模型的Shearlet域SAR圖像去噪算法,所提出的算法既利用了稀疏系數間的空間相關性,又基于貝葉斯模型獲取了動態(tài)的噪聲閾值,在實現(xiàn)噪聲濾波的同時可以有效的保持邊緣信息。首先對對數變換后的SAR圖像進行Shearlet稀疏表示,其次根據稀疏系數的統(tǒng)計特性利用貝葉斯模型進行噪聲檢測的建模,最后利用自適應加權收縮實現(xiàn)SAR圖像噪聲像素的平滑處理。在MSTAR數據庫上的實驗結果驗證了所提方法的可行性和有效性。3.提出了一種基于動態(tài)稀疏K-SVD(DSK-SVD)的字典學習方法。該算法的突出優(yōu)點在于能夠動態(tài)的計算稀疏編碼的稀疏度,并對字典原子進行并行更新。首先,利用字典的互相關來定義稀疏編碼過程中的稀疏度,用來動態(tài)的控制稀疏系數的稀疏度。其次,利用并行原子更新準則實現(xiàn)字典更新過程中的字典原子和稀疏系數的更新。在MSTAR數據庫和HRRP數據上的實驗結果驗證了所提方法的可行性和有效性。4.提出了一種基于D-S證據迭代折扣方法的雷達目標融合識別方法,該方法對利用DSK-SVD算法對訓練樣本的特征進行學習,并利用測試樣本的重構誤差來定義基本概率分配(BPA)函數。首先,利用混淆矩陣以及BPA函數,計算出各個證據對應的折扣因子;其次,利用每次迭代得到的折扣因子重復對證據源進行修正,直到沖突系數小于給定的閾值;最后,利用修正后的證據進行融合識別。與其它典型融合識別方法相比,本文提出的方法在小樣本情況下能夠保持較好的識別性能。
[Abstract]:Radar target recognition technology is based on radar echo signal to extract information related to target characteristics to achieve target attribute or category determination.With the development of international situation, radar target recognition is more and more favored by researchers all over the world.With the application of high resolution radar system, it is possible to obtain more detailed geometric structure information and detail information.As a typical high resolution radar signal, high resolution Resolution radar (HRR) and synthetic Aperture radar (SAR) echo signal HRRP and SAR image have also become the hot spot of radar target recognition in many countries.On the basis of sparse learning theory, the radar target recognition method based on HRRP target and SAR image target is studied in this paper. The main research work is as follows: 1.The sparse learning theory is studied.Firstly, three typical sparse modeling methods, three classical sparse solving methods and the application of sparse learning are described. Secondly, the sparse representation methods of HRRP targets and SAR images are studied, respectively.And its sparsity is analyzed. 2.A SAR image denoising algorithm in Shearlet domain based on Bayesian model is proposed. The proposed algorithm not only utilizes the spatial correlation between sparse coefficients, but also obtains the dynamic noise threshold based on Bayesian model.The edge information can be effectively maintained while noise filtering is realized.Firstly, the SAR image after logarithmic transformation is represented by Shearlet sparse representation; secondly, the Bayesian model is used to model the noise detection according to the statistical characteristics of the sparse coefficient; finally, the noise pixel smoothing of the SAR image is realized by adaptive weighted shrinkage.The experimental results on MSTAR database show that the proposed method is feasible and effective.This paper presents a dictionary learning method based on dynamic sparse K-SVD-DSK-SVD.The outstanding advantage of this algorithm is that it can dynamically calculate the sparse degree of sparse coding and update dictionary atoms in parallel.Firstly, the sparse degree in the process of sparse coding is defined by the cross-correlation of dictionaries, which is used to control the sparsity of sparse coefficients dynamically.Secondly, the parallel atomic update criterion is used to update the dictionary atoms and sparse coefficients in the process of dictionary updating.The experimental results on MSTAR database and HRRP data show that the proposed method is feasible and effective.A method of radar target fusion recognition based on D-S evidence iterative discount method is proposed. This method studies the features of training samples using DSK-SVD algorithm, and defines the basic probability allocation (BPA) function by using the reconstruction error of test samples.First, the discounted factors corresponding to each evidence are calculated by using the confusion matrix and the BPA function. Secondly, the discounted factors obtained by each iteration are used to modify the evidence source repeatedly until the conflict coefficient is less than the given threshold.The modified evidence is used for fusion recognition.Compared with other typical fusion recognition methods, the proposed method can maintain better recognition performance in the case of small samples.
【學位授予單位】：南京航空航天大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TN957.52

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