基于局部特性的毫米波距離像識(shí)別方法研究
發(fā)布時(shí)間：2019-07-03 20:56
【摘要】：雷達(dá)自動(dòng)目標(biāo)識(shí)別技術(shù)是目標(biāo)探測(cè)和精確制導(dǎo)等應(yīng)用的關(guān)鍵技術(shù)之一。高分辨距離像作為一類重要的雷達(dá)目標(biāo)識(shí)別信號(hào),能夠反映出目標(biāo)在雷達(dá)視線上的強(qiáng)散射點(diǎn)分布情況。毫米波雷達(dá)容易實(shí)現(xiàn)大帶寬的發(fā)射信號(hào),可提高距離分辨能力,從而能夠獲得更多的目標(biāo)細(xì)節(jié)特征,有利于實(shí)現(xiàn)精確的目標(biāo)識(shí)別。然而,距離像受雷達(dá)參數(shù)、目標(biāo)狀態(tài)、背景環(huán)境以及天氣等因素的影響,呈現(xiàn)出高度的非線性特點(diǎn),使用傳統(tǒng)的線性方法進(jìn)行距離像識(shí)別并不能得到滿意的結(jié)果。流形學(xué)習(xí)是一種被廣泛研究的非線性維數(shù)約減方法,能夠從高維的非線性特征空問中發(fā)現(xiàn)線性的低維特征結(jié)構(gòu)。論文針對(duì)地面目標(biāo)的毫米波距離像識(shí)別問題,基于流形學(xué)習(xí)方法,從特征選擇、分類器設(shè)計(jì)、主動(dòng)學(xué)習(xí)和非平衡學(xué)習(xí)等四個(gè)方面展開了研究工作,主要研究?jī)?nèi)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(gòu)誤差排列的非監(jiān)督特征選擇算法、基于標(biāo)簽重構(gòu)拉普拉斯得分的半監(jiān)督特征選擇算法和基于改進(jìn)約束得分的半監(jiān)督特征選擇算法�；诰植恐貥�(gòu)誤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(gòu)誤差得到最優(yōu)局部特征序列,再通過排列技術(shù)得到全局特征序列。基于標(biāo)簽重構(gòu)拉普拉斯得分的特征選擇算法利用標(biāo)簽重構(gòu)技術(shù)將基于拉普拉斯得分的特征選擇算法推廣到半監(jiān)督應(yīng)用場(chǎng)合,同時(shí)利用測(cè)地距離代替歐氏距離來(lái)度量非線性特征空間中的樣本相似度。在基于改進(jìn)約束得分的特征選擇算法中,假設(shè)對(duì)約束條件和樣本的局部特性并非完全獨(dú)立,而是存在一定聯(lián)系,通過已知的對(duì)約束條件能夠改進(jìn)樣本的局部特性,并利用改進(jìn)后的局部特性和對(duì)約束條件進(jìn)行特征選擇。在設(shè)計(jì)分類器時(shí),針對(duì)距離像的方位敏感性問題,提出了基于測(cè)地權(quán)重稀疏重構(gòu)的分類算法。算法假設(shè)同一目標(biāo)的距離像樣本在歸一化之后分布在一個(gè)單位超球面的子流形上,通過小方位角范圍內(nèi)的樣本具有高相關(guān)性的特點(diǎn),對(duì)這些子流形進(jìn)行分類。首先使用改進(jìn)的測(cè)地距離計(jì)算所有樣本之間的相似度。然后計(jì)算測(cè)地權(quán)重樣本,測(cè)地權(quán)重樣本能夠?qū)⒊蛎嫔系淖恿餍握归_,把非線性的樣本結(jié)構(gòu)變換成線性結(jié)構(gòu)。最后將所有標(biāo)簽已知樣本作為字典,利用標(biāo)簽重構(gòu)技術(shù)估計(jì)標(biāo)簽未知樣本的類別概率。在傳統(tǒng)的距離像識(shí)別方法中,用于訓(xùn)練分類器的樣本通過隨機(jī)選擇獲得。針對(duì)同一種分類器模型,不同的訓(xùn)練樣本可能會(huì)訓(xùn)練出不同的分類器參數(shù),而這些參數(shù)不同的分類器的性能也可能相差很大。主動(dòng)學(xué)習(xí)的目的是在給定的訓(xùn)練樣本集中選擇一個(gè)訓(xùn)練樣本子集,當(dāng)用這個(gè)子集訓(xùn)練分類器時(shí),可以獲得最優(yōu)的分類器。論文針對(duì)距離像識(shí)別中的主動(dòng)學(xué)習(xí)問題,研究了基于局部線性重構(gòu)的主動(dòng)學(xué)習(xí)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(gòu)矩陣來(lái)描述樣本的局部結(jié)構(gòu),以最優(yōu)重構(gòu)的方法來(lái)選擇訓(xùn)練樣本,得到了基于拉普拉斯直推優(yōu)化設(shè)計(jì)的主動(dòng)學(xué)習(xí)算法,并比較了幾種主動(dòng)學(xué)習(xí)算法在距離像識(shí)別中的效果。非平衡學(xué)習(xí)是模式識(shí)別理論在實(shí)際應(yīng)用中遇到的問題,它更關(guān)注小類樣本的識(shí)別能力。當(dāng)用于訓(xùn)練分類器的樣本數(shù)量非平衡時(shí),分類面會(huì)向小類樣本移動(dòng),從而降低小類樣本的識(shí)別率。論文針對(duì)非平衡數(shù)據(jù)條件下的距離像識(shí)別問題,提出基于代價(jià)敏感測(cè)地約束得分的半監(jiān)督特征選擇算法。算法引入代價(jià)敏感技術(shù),將基于約束得分的特征選擇算法推廣到非平衡學(xué)習(xí)場(chǎng)合,然后通過約束重構(gòu)技術(shù),將其推廣到半監(jiān)督應(yīng)用場(chǎng)合,使之適用于非平衡數(shù)據(jù)分布條件下的半監(jiān)督分類問題,提高小類樣本的識(shí)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(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN957.52
本文編號(hào)：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(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN957.52
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