LSTM recurrent neural networks for high resolution range profile based radar target classification

Positive and timely identification of targets is critical in any military scenario. Target identification from backscattered electromagnetic energy is an evolving area. The objective of this paper is to study the applicability of Long Short-Term Memory Recurrent Neural Network (LSTM RNN) for High Resolution Range Profile (HRRP) based Radar target classification. Simulated Radar Range Profile data is used here. Three Different Target models are considered in this study. The classification is performed using a LSTM RNN.