Wideband Radar Target Polarimetric Feature Extraction And Recognition Method Based On Kernel Method

Radar target feature extraction and classifier design are key problems in radar target recognition system. In order to extract the features reflecting target's characteristic, construct the optimal classifier and improve radar target recognition performance, this dissertation regards the plane, tank and ship as the recognizing objects, focuses on polarimetric feature extraction, optimal selection, kernel methods classifiers model optimization and design based on wideband high-resolution fully polarimetric radar. The whole work contains three parts:1. Radar Target HRRP Polarimetric Feature Extraction and Optimal Selection(1) Polarimetric features extraction and optimal selection in radar target HRRP are mainly researched in three different aspects:â‘ the entropy is introduced into fully polarimetric HRRP, which is usually used in polarimetric SAR, then the entropy feature based on HRRP is proposed, these parameters reflect the HRRP's degree of scattering randomness;â‘¡according to similarity theory between two Sinclair scattering matrix, six probability description features are defined between HRRPs and some standard objects, the parameters reflect the target's physics structure;â‘¢the similarity feature based on Mueller matrix is proposed, which reflects the target power characteristic. It is also proved that this feature does not vary with the orientation angle.(2) The formulas of the three kinds of polarimetric feature are deduced in fully and dual polarimetric radar. The results revel that every feature formula is different in fully and dual polarimetric radar, some of them are correlative linearly. To verify the features'utility, some experiments had been done in feature separability and recognition performance by using some experimental data on planes and ships. The experimental results present that the features of HRRP proposed in dissertation are robust and well separability.2. The Study on the Separability of SVM and Model Optimal Selection Used in Radar Target Recognition(1) The sufficient necessary condition of linear separability is proved using a brief and clear method, and also proved and explained the new essential of penalty factor in SVM. The effects on the recognition performance of kernel function and its parameters are quantitatively analyzed.(2) The essential of model optimal selection is explained and the method of model single parameter selection is analyzed. Some limitations of single parameter model are presented, and then the connotation and necessity of SVM model multi-parameter selection are theoretically analyzed. SVM model optimal multi-parameter selection method for imbalanced data recognition is proposed. Some experiments on Benchmarks and radar target polarimetric features verify that the new method can improve the recognition performance effectively.3. Kernel Method Classifier Design for Radar Target Recognition and Kernel Matrix Construction(1) Some misclassification and reject classification problems caused by the standard SVDD decision strategy always occur especially in lack of separability of radar target, so radar target fuzzy recognition method is proposed based on SVDD. Experimental results on the fully polarimetric HRRP data present that the fuzzy recognition method can achieve better accuracy than standard SVDD.(2) Lack of separability of radar target can also cause misclassification and reject classification problems using SVM. To deal with these problems, the improved decision strategy is proposed based on FSVM. Simulations verify the new method.(3) To deal with the linear non-separability, the new conception and method of kernel matrix contraction are proposed. The main idea of the new method is to make the non-separable data separability by changing the construction of data in feature space. The kernel matrix of contracted pattern is deduced, the separability of contracted pattern is also proved more excellent than before. The experiments conducted to evaluate the performance of the new method are mainly in two aspects: the measurement and classifier performance of kernel matrix. The results on planes and ships HRRP data reveal that the performance of contracted kernel matrix is more excellent than before.