Feature Extraction And Recognition Technology Of Space Target

Currently, rapid and drastic changes are taking place in the space technology. With the reinforcement of depending on space in modern wars and the development of spaceflight activity of human, the surveillance and recognition of space targets become especially important. Most of active radars, used to measure target's characteristic, belong to low-resolution narrowband radar. The information obtained by low-resolution narrowband is limited, but it was still a main sort of space target detect sensor for low cost. The range resolution of narrowband radar is low. Influenced by different target scattering characteristics, there are differences in echo envelop of different targets, which could be used for target recognition. The movement of space target is regular, so variation of its pose is certain. The research starting point of this thesis lies that satisfactory results can achieved by using narrowband radar to recognize space target.In this thesis, the radar echo characteristic and the identification of space target are studied, and the distribution of measured data is analyzed. Their central moment's features, with translation-invariant property, are calculated for further recognition. Then Principle Component Analysis (PCA) and Fisher Linear Discriminant (FLD) are respectively used to choose the central moment feature. Finally, the Least Euclidean Distance and a multi-class support vector machine (SVM) classifier are respectively designed to classify space targets. The experimental comparisons based on measured satellites data show that the space target can be recognized by utilizing its radar echo envelop.