| 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 scatters distribution along the radar line-of-sight, which contains potentially discriminative information about the target geometry. Furthermore, the HRRP can be easily acquire and also 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.Several methods based-on kernel function of radar target recognition are intensively and extensively studied in this paper. The main contents are as follows:1. The scatter-center model is discussed. The Six kinds of simulated point targets are designed and the range profiles at aspect angle are computed.2. The null-based LDA take full advantage of the null space while the other methods remove the null space, but it can not to resolve the large sample size problem. A new null space method is discuss, which is simpler than all other null space approaches and is also applicable to the large sample size problem.3. A two-phase KFD method: kernel principal component analysis(KPCA) plus Fisher linear discriminant analysis (LDA). This framework provides novel insights into the nature of KFD, it can make full use of two kinds of discriminant information, regular and irregular, and it has a better classification performance than single method.4. Proposes a novel approach, which constructs a between-class scatter matrix and a within-class scatter by use of Kernel Support Vectors (KVs). In additional, the null-space Fisher method is exploited to calculate the optimal transform matrix. This method achieves good recognition performance for HRRP.All of these methods are proved by experiments on simulated data and real data of planes. These methods can be applied to automatic and real time recognition systems. |
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