Based Radar Target Distance, Time-varying Characteristics And Nuclear Methods Like Identification Study

As one of the important developments of modern radar, radar automatic target recognition (RATR) shows great strength in many areas. RATR will be an important component of future weapon systems and is focused at home and abroad, it has a wide range of civil and military values.RATR using high resolution range profiles (HRRPs) is researched in this disertation. An overview of the background and significance of RATR is shown first. Then, feature extraction and classification techniques in HRRP-based RATR are summarized and studied.Based on the scattering center model, we analysis the electromagnetic scattering characteristics and scattering mechanisms of aircrafts, and the formation mechanism and characteristics of HRRPs. HRRPs of eight aircraft categories within a certain attitude angle range are simulated. Next, some preprocessing methods including distance alignment and normalization are summarized.We use two types of methods in feature extraction. One type is the subspace methods, including principal component analysis (PCA), linear discriminant analysis (LDA), kernel-based principal component analysis (KPCA) and kernel-based fisher discriminant analysis (KFDA), and the other one is time-frequency analysis, wherein we try to extract instantaneous features or applying image processing methods to extract features.Whereafter, several classifiers including the Euclidea distance, Radial Basis Function neural network (RBFNN), Support Vector Machine (SVM), and Kernel-based Nonlinear Representor (KNR) are adopted and compared.Experimental results on both simulated and measured HRRPs show comparatively good performance of the techniques.