| The increasing availability of high resolution range (HRR) radars provides a new way for radar target recognition. High resolution range profile (HRRP) shows the target's scatterers distribution along the radar line-of-sight, which contains potentially discriminative information about the target geometry. Furthermore, the HRRP can be easily captured and avoids the complex motion compensation processing, relative to two-dimensional or three-dimensional imagery. Therefore, HRR radar target recognition has received extensive attention from the radar technique community in recent years.Based on the previous work, this dissertation is focused on the feature extraction and classification of a radar target recognition system using HRRP. Some new methods are presented, and all of them are evaluated on both simulated and measured data of aircrafts.The main content is summarized as follows:1. According to the perturbation theory and null-space method, two feature extraction methods for radar HRRP recognition are proposed respectively. One is QR decomposition based linear discriminant analysis (LDA), the other is direct LDA. Meanwhile, both methods are generalized to nonlinear versions via kernel trick. The experimental comparisons show that QR decomposition based methods have great advantage in terms of real-time performance, while another two achieve excellent recognition performance.2. The classical Gram-Schmidt (GS) orthogonalization procedure is very sensitive to round-off-errors. Thereby, a modified GS orthogonalization procedure using kernel function operator (KMGS) is first proposed. Then two nonlinear algorithms, batch and class-incremental kernel discriminant analysis (KDA), are put forward for radar HRRP recognition. Compared with other kernel-based methods, batch KDA and class-incremental KDA both achieve good recognition performance for making use of the significant discriminative information in the null space of within-class scatter matrix. Moreover, class-incremental KDA introduces an incremental approach to update the discriminant vectors when new target data sets are inserted into the training set, which is very desirable for designing a dynamic recognition system. Therefore, it has apparent advantage in real-time performance.3. In pattern analysis, the common principle of feature extraction is desirable to extract feature vectors with uncorrelated attributes. Motivated by this principle, a new formulation for KDA is proposed for radar HRRP recognition, which can solve the uncorrelated discriminant vectors by joint diagnonalization and GSVD respectively. The methods both achieve good recognition performance for removing the redundancy among feature vectors extracted.4. It is well known that classical Fisher discriminant analysis algorithms suffer from singularity problem and lose some significant discriminative information. To address this problem, one conclusion that there exists no useful discriminative information in the null space of the population scatter matrix is first derived, which can be used to reduce the dimensionality of original scatter matrices as well as the computation complexity of the following operation. Then a double discriminant subspaces algorithm for radar HRRP recognition is proposed. The new method considers the separability from a global viewpoint to some extent, which can make full use of the discriminative information in both null space and non-null space of within-class scatter matrix. Therefore, it makes the new method a more powerful discriminator.5. Kernel nonlinear classifiers from classical nearest feature line and nearest feature plane are proposed for radar HRRP classification, which can directly classify original range profiles and need no feature extraction beforehand. Meanwhile, these classifiers are modified based on locally nearest neighborhood rule. Compared with those original ones, the modified classifiers achieve competitive performance and take much lower computation cost, while the probability of failure is reduced to some extent.
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