Target Classification and Prediction of Unguided Rocket Trajectories Using Deep Neural Networks

Abstract

This paper deals with classification and prediction of low-altitude rocket targets using deep neural networks. Conventionally, model-based methods such as the Kalman filter are widely used. However, they can lack robustness due to unexpected situations and noisy sensor measurements. To address this issue, this study proposes the use of various data-driven methods which are powerful on situations where no model is available. Specifically, three types of neural networks are used: DNN (deep neural network), CNN (convolutional neural network) and RNN (recurrent neural network). To verify the benefit and robustness of the proposed algorithms, comparisons with the model-based method are performed on several scenarios