基于局部特性的毫米波距離像識別方法研究
發(fā)布時間：2019-07-03 20:56
【摘要】：雷達自動目標識別技術是目標探測和精確制導等應用的關鍵技術之一。高分辨距離像作為一類重要的雷達目標識別信號,能夠反映出目標在雷達視線上的強散射點分布情況。毫米波雷達容易實現大帶寬的發(fā)射信號,可提高距離分辨能力,從而能夠獲得更多的目標細節(jié)特征,有利于實現精確的目標識別。然而,距離像受雷達參數、目標狀態(tài)、背景環(huán)境以及天氣等因素的影響,呈現出高度的非線性特點,使用傳統(tǒng)的線性方法進行距離像識別并不能得到滿意的結果。流形學習是一種被廣泛研究的非線性維數約減方法,能夠從高維的非線性特征空問中發(fā)現線性的低維特征結構。論文針對地面目標的毫米波距離像識別問題,基于流形學習方法,從特征選擇、分類器設計、主動學習和非平衡學習等四個方面展開了研究工作,主要研究內容如下：從算法的角度研究了距離像特征選擇問題,提出了基于局部重構誤差排列的非監(jiān)督特征選擇算法、基于標簽重構拉普拉斯得分的半監(jiān)督特征選擇算法和基于改進約束得分的半監(jiān)督特征選擇算法�；诰植恐貥嬚`差排列的特征選擇算法可以看作是特征選擇版本的局部線性嵌入,通過最小化局部重構誤差得到最優(yōu)局部特征序列,再通過排列技術得到全局特征序列�；跇撕炛貥嬂绽沟梅值奶卣鬟x擇算法利用標簽重構技術將基于拉普拉斯得分的特征選擇算法推廣到半監(jiān)督應用場合,同時利用測地距離代替歐氏距離來度量非線性特征空間中的樣本相似度。在基于改進約束得分的特征選擇算法中,假設對約束條件和樣本的局部特性并非完全獨立,而是存在一定聯系,通過已知的對約束條件能夠改進樣本的局部特性,并利用改進后的局部特性和對約束條件進行特征選擇。在設計分類器時,針對距離像的方位敏感性問題,提出了基于測地權重稀疏重構的分類算法。算法假設同一目標的距離像樣本在歸一化之后分布在一個單位超球面的子流形上,通過小方位角范圍內的樣本具有高相關性的特點,對這些子流形進行分類。首先使用改進的測地距離計算所有樣本之間的相似度。然后計算測地權重樣本,測地權重樣本能夠將超球面上的子流形展開,把非線性的樣本結構變換成線性結構。最后將所有標簽已知樣本作為字典,利用標簽重構技術估計標簽未知樣本的類別概率。在傳統(tǒng)的距離像識別方法中,用于訓練分類器的樣本通過隨機選擇獲得。針對同一種分類器模型,不同的訓練樣本可能會訓練出不同的分類器參數,而這些參數不同的分類器的性能也可能相差很大。主動學習的目的是在給定的訓練樣本集中選擇一個訓練樣本子集,當用這個子集訓練分類器時,可以獲得最優(yōu)的分類器。論文針對距離像識別中的主動學習問題,研究了基于局部線性重構的主動學習算法,并在該算法的理論框架下,使用拉普拉斯矩陣代替局部線性重構矩陣來描述樣本的局部結構,以最優(yōu)重構的方法來選擇訓練樣本,得到了基于拉普拉斯直推優(yōu)化設計的主動學習算法,并比較了幾種主動學習算法在距離像識別中的效果。非平衡學習是模式識別理論在實際應用中遇到的問題,它更關注小類樣本的識別能力。當用于訓練分類器的樣本數量非平衡時,分類面會向小類樣本移動,從而降低小類樣本的識別率。論文針對非平衡數據條件下的距離像識別問題,提出基于代價敏感測地約束得分的半監(jiān)督特征選擇算法。算法引入代價敏感技術,將基于約束得分的特征選擇算法推廣到非平衡學習場合,然后通過約束重構技術,將其推廣到半監(jiān)督應用場合,使之適用于非平衡數據分布條件下的半監(jiān)督分類問題,提高小類樣本的識別率。
[Abstract]:Radar automatic target recognition technology is one of the key technologies of target detection and accurate guidance. The high-resolution distance image can be used as a kind of important radar target identification signal, which can reflect the distribution of the strong scattering point of the target in the radar's line of sight. The millimeter-wave radar is easy to realize large-bandwidth transmission signals and can improve the distance-resolution capability, so that more target detail characteristics can be obtained, and the accurate target recognition can be realized. However, the distance image is affected by the parameters of the radar, the target state, the background environment and the weather. Manifold learning is a widely studied nonlinear dimension reduction method, which can find a linear low-dimensional feature structure from a high-dimensional nonlinear feature space-to-question. Aiming at the problem of millimeter wave distance image recognition of the ground target, the research work is carried out from four aspects, such as feature selection, classifier design, active learning and non-equilibrium learning, based on the manifold learning method. The main contents of the study are as follows: A non-supervised feature selection algorithm based on the local reconstruction error arrangement, a semi-supervised feature selection algorithm based on the label reconstruction Laplacian score and a semi-supervised feature selection algorithm based on the improved constraint score are proposed. The feature selection algorithm based on the partial reconstruction error arrangement can be regarded as the local linear embedding of the feature selection version, the optimal local characteristic sequence is obtained by minimizing the local reconstruction error, and the global feature sequence is obtained through the arrangement technology. The feature selection algorithm based on the label reconstruction Laplacian score is used to extend the feature selection algorithm based on the Laplacian score to the semi-supervised application, and the similarity of the samples in the non-linear feature space is measured by the geodesic distance instead of the Euclidean distance. in the feature selection algorithm based on the improved constraint score, it is assumed that the local characteristic of the constraint condition and the sample is not completely independent, but there is a certain connection, and the local characteristic of the sample can be improved by the known constraint conditions, And feature selection is carried out using the improved local characteristics and the constraint conditions. In the design of the classifier, the classification algorithm based on the weight-sparse reconstruction of the geodesic is proposed for the orientation sensitivity of the distance image. The algorithm assumes that the distance image samples of the same object are distributed on a submanifold of a unit hypersphere after normalization, and the submanifolds are classified by the characteristics of high correlation in the samples in the small azimuth range. First, the similarity between all samples is calculated using an improved geodesic distance. Then, the weight sample of the geodesic is calculated, the submanifold on the hypersphere can be expanded, and the non-linear sample structure is transformed into a linear structure. And finally, all the labels are known as a dictionary, and the category probability of the tag unknown sample is estimated by the label reconstruction technique. In a conventional distance image recognition method, a sample for training a classifier is obtained by random selection. For the same classifier model, different training samples may train different classifier parameters, and the performance of the classifiers with different parameters may also differ greatly. The purpose of active learning is to select a subset of training samples in a given training sample set. When using this subset to train the classifier, the optimal classifier can be obtained. In this paper, the active learning algorithm based on local linear reconstruction is studied for distance image recognition, and the local structure of the sample is described by using the Laplacian matrix instead of the local linear reconstruction matrix under the theoretical framework of the algorithm. The optimal reconstruction method is used to select the training samples, and the active learning algorithm based on the Laplacian direct-push optimization design is obtained, and the effect of several active learning algorithms in distance image recognition is compared. Unbalanced learning is the problem of pattern recognition theory in practical application, and it is more concerned with the identification ability of small-class samples. When the number of samples used to train the classifier is not balanced, the classification surface moves to the small-class sample, thereby reducing the recognition rate of the small-class sample. In this paper, a semi-supervised feature selection algorithm based on cost-sensitive constraint score is proposed for distance image recognition under the condition of non-equilibrium data. In this paper, the cost-sensitive technique is introduced, the feature selection algorithm based on the constraint score is extended to the non-equilibrium learning situation, and then the constraint reconstruction technique is applied to the semi-supervised application, so that it can be applied to the semi-supervised classification problem under the non-equilibrium data distribution condition, And the recognition rate of the small sample is improved.
【學位授予單位】：南京理工大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TN957.52
本文編號：2509662
[Abstract]:Radar automatic target recognition technology is one of the key technologies of target detection and accurate guidance. The high-resolution distance image can be used as a kind of important radar target identification signal, which can reflect the distribution of the strong scattering point of the target in the radar's line of sight. The millimeter-wave radar is easy to realize large-bandwidth transmission signals and can improve the distance-resolution capability, so that more target detail characteristics can be obtained, and the accurate target recognition can be realized. However, the distance image is affected by the parameters of the radar, the target state, the background environment and the weather. Manifold learning is a widely studied nonlinear dimension reduction method, which can find a linear low-dimensional feature structure from a high-dimensional nonlinear feature space-to-question. Aiming at the problem of millimeter wave distance image recognition of the ground target, the research work is carried out from four aspects, such as feature selection, classifier design, active learning and non-equilibrium learning, based on the manifold learning method. The main contents of the study are as follows: A non-supervised feature selection algorithm based on the local reconstruction error arrangement, a semi-supervised feature selection algorithm based on the label reconstruction Laplacian score and a semi-supervised feature selection algorithm based on the improved constraint score are proposed. The feature selection algorithm based on the partial reconstruction error arrangement can be regarded as the local linear embedding of the feature selection version, the optimal local characteristic sequence is obtained by minimizing the local reconstruction error, and the global feature sequence is obtained through the arrangement technology. The feature selection algorithm based on the label reconstruction Laplacian score is used to extend the feature selection algorithm based on the Laplacian score to the semi-supervised application, and the similarity of the samples in the non-linear feature space is measured by the geodesic distance instead of the Euclidean distance. in the feature selection algorithm based on the improved constraint score, it is assumed that the local characteristic of the constraint condition and the sample is not completely independent, but there is a certain connection, and the local characteristic of the sample can be improved by the known constraint conditions, And feature selection is carried out using the improved local characteristics and the constraint conditions. In the design of the classifier, the classification algorithm based on the weight-sparse reconstruction of the geodesic is proposed for the orientation sensitivity of the distance image. The algorithm assumes that the distance image samples of the same object are distributed on a submanifold of a unit hypersphere after normalization, and the submanifolds are classified by the characteristics of high correlation in the samples in the small azimuth range. First, the similarity between all samples is calculated using an improved geodesic distance. Then, the weight sample of the geodesic is calculated, the submanifold on the hypersphere can be expanded, and the non-linear sample structure is transformed into a linear structure. And finally, all the labels are known as a dictionary, and the category probability of the tag unknown sample is estimated by the label reconstruction technique. In a conventional distance image recognition method, a sample for training a classifier is obtained by random selection. For the same classifier model, different training samples may train different classifier parameters, and the performance of the classifiers with different parameters may also differ greatly. The purpose of active learning is to select a subset of training samples in a given training sample set. When using this subset to train the classifier, the optimal classifier can be obtained. In this paper, the active learning algorithm based on local linear reconstruction is studied for distance image recognition, and the local structure of the sample is described by using the Laplacian matrix instead of the local linear reconstruction matrix under the theoretical framework of the algorithm. The optimal reconstruction method is used to select the training samples, and the active learning algorithm based on the Laplacian direct-push optimization design is obtained, and the effect of several active learning algorithms in distance image recognition is compared. Unbalanced learning is the problem of pattern recognition theory in practical application, and it is more concerned with the identification ability of small-class samples. When the number of samples used to train the classifier is not balanced, the classification surface moves to the small-class sample, thereby reducing the recognition rate of the small-class sample. In this paper, a semi-supervised feature selection algorithm based on cost-sensitive constraint score is proposed for distance image recognition under the condition of non-equilibrium data. In this paper, the cost-sensitive technique is introduced, the feature selection algorithm based on the constraint score is extended to the non-equilibrium learning situation, and then the constraint reconstruction technique is applied to the semi-supervised application, so that it can be applied to the semi-supervised classification problem under the non-equilibrium data distribution condition, And the recognition rate of the small sample is improved.
【學位授予單位】：南京理工大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TN957.52
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