1D CNN-GRU 기반 불특정 비행 거리 궤적을 가진 미사일 분류를 위한 학습 방법에 관한 연구

Abstract

In this paper, we propose a training method and an optimal training distance interval to improve target classification performance based on the one-dimensional convolutional neural network-gated recurrent unit (1D CNN-GRU). To improve classification performance, we describe two training methods and confirm classification performance. The structure and setting parameters of 1D CNN-GRU use the results optimized in the previous study. We analyze classification performance at intervals of 10km from 20 km to 80 km by training the network. As a result of applying the proposed training method, the high classification performance at all training distances for unfixed flight range trajectories was obtained at over 89.53 % in the absence of noise and over 80.38 % in the case of severe noise, respectively. In addition, considering the training efficiency and target classification accuracy, it was confirmed that 50 km was suitable for training distance intervals with the average classification accuracy of 99.70 % in non-noise and 89.19 % in the case of severe noise.