Hrrp Recognition Method Based On Kernel Clustering

Automatic target recognition is one of the significantly developing aspects in modern radar system. The high range resolution profile (HRRP) contains the more information of target's features. So target recognition based on HRRP has become more and more important, the kernel-based methods have been widely applied in radar target recognition. Recently the kernel method has been used in clustering analysis. This thesis mainly investigates the classification mechanism based on kernel clustering method.At first some techniques of feature extraction and classification are discussed, and the kernel method is introduced. The main work and basic framework of the dissertation are also presented.The obtaining of HRRP based on scatter-center model is introduced in chapter two, then the sensitivity is analysed. Lastly the right methods of pretreatment and feature extraction are given.In chapter three the HRRP recognition method based on kernel C-means clustering is investigated. Firstly point out the necessary of parameter optimizing, discuss the shortage of existing method, then an improved optimizing algorithm is presented. The validity of this measure has been proved by simulation experiments on standard data.Based on the analysis of kernel C-means clustering method, the HRRP recognition method based on kernel C-means clustering is proposed. Then this method is applied in HRRP recognition to validate the performance. Experiments indicate that this algorithm can obtain the best clustering number by the training results. And the best parameter and the optimal results are also obtained by this algorithm. Further more, the effect by feature extraction is better.In chapter four the HRRP recognition algorithm based on fuzzy kernel clustering is studied. Firstly the basic mechanism of kernel fuzzy C-means clustering method is introduced, and the fuzzy kernel clustering self-adaptive algorithm is discussed. Then one HRRP recognition method based on fuzzy clustering is proposed. According to the properties of the range profiles, mixture of kernels is presented. Experiments show that the algorithm based on this new mixture of kernels and Gauss kernel can both decide the clustering number, but the first one has higher classification rate.Chapter five summarizes the whole paper, and points the problems which need to be improved.