탄도미사일의 중간단계에서 탄두와 기만체간의 효과적인 변별 연구

Abstract

For effective detection and tracking of ballistic missiles in the mid-course phase, the discrimination problem between the warhead and decoy needs to be solved. However, because these targets are significantly similar in shape, it is extremely difficult to discriminate them using the representative radar information, such as radar cross-sections and high-resolution range profiles. For this reason, the micro-Doppler information of an echo signal received from a target with micro-motion is usually exploited, in addition to the time-frequency(TF) transform method, to achieve more effective discrimination. However, in conventional methods, only the short time Fourier transform and image moment have been considered as the TF transform and feature extraction methods, respectively, leading to extreme computational complexity. Therefore, in this study, we conducted various simulations to determine the most efficient algorithm from combinations comprising three stages: TF transform, feature extraction, and feature classification. The simulation results obtained using CAD models and an electromagnetic prediction technique reveal that the combination of the Zao-Atlas-Marks distribution with a three-dimensional feature vector and neural network classifier is the most appropriate.