Study On Radar Target Recognition Using Range Profiles Based On Kernel Methods And Manifold Learning

Radar target recognition plays an important role in modern radar, and finds its ways to wide military and civilian applications. High resolution range profiles contain more structural information of a target, and are easy to obtain and process, and thus provide us with a more reliable tool for target recognition. Currently, kernel method and manifold learning, among other methods, become two focuses in the field of pattern recognition and machine learning. Kernel method shows many advantages as to solve nonlinear problems, while manifold learning aims to discover the intrinsic characteristics of high-dimensional data.In this dissertation, the above two methods are studied, and in order to overcome the weakness of existing methods, several generalized and improved algorithms are proposed and applied to radar target recognition using range profiles. The main contents and innovations of this dissertation are summarized as follows.1. Kernel discriminant analysis and its variants are studied intensively, and an optimal kernel discriminant analysis (OKDA) is given and adopted to extract nonlinear discriminative features from range profiles. Experimental results show the good recognition performance of OKDA.2. Kernel uncorrelated discriminant analysis is studied for feature extraction from range profiles. By analyzing the statistically uncorrelated property further and introducing a kernel function, a kernel uncorrelated Fisher criterion is derived, and two equivalent algorithms, called direct kernel uncorrelated discriminant analysis (DKUDA) and kernel uncorrelated discriminant analysis based on generalized singular value decomposition (KUDA/GSVD), are proposed to extract statistically uncorrelated discriminative features from range profiles. As compared with uncorrelated discriminant analysis (UDA) and kernel uncorrelated discriminant analysis (KUDA), DKUDA and KUDA/GSVD can reduce large amount of computation. Moreover, the singularity problem is resolved effectively.3. Manifold learning is studied for feature extraction from range profiles. Based on the analysis of several classical manifold learning methods, a supervised nonlinear manifold learning algorithm, named supervised kernel neighborhood preserving projections (SKNPP), is proposed to extract nonlinear features from range profiles. SKNPP is obtained by modifying NPP with class label information and furtherextending it to nonlinear form by utilizing kernel function. It can not only preserve the within-class neighboring geometry in high-dimensional space, but also gain a perfect nonlinear approximation of data manifold.4. Two novel nonlinear manifold learning algorithms, called kernel uncorrelated discriminative neighborhood embedding (KUDNE) and kernel uncorrelated discriminative locality preserving projections (KUDLPP), are proposed to extract features from range profiles. They are obtained by combing supervised kernel neighborhood preserving projections (SKNPP) and supervised kernel locality preserving projections (SKLPP) with kernel discriminant analysis (KDA), respectively, under the statistically uncorrelated constraint. The two algorithms can not only preserve the within-class geometry, but also maximizing the between-class scatter of projected samples. Moreover, the extracted feature space contains minimum redundancy.5. Three kernel-based nonlinear classifiers, including support vector machine (SVM), kernel-based nonlinear discriminator (KND) and kernel-based nonlinear representor (KNR) are studied and compared in detail, and applied for radar target recognition with range profiles. Especially, KND and KNR are two novel kernel-based nonlinear classifiers, which can achieve satisfactory recognition performance, in terms of both recognition accuracy and recognition speed, as compared with SVM.