Battlefield Situation Awareness Model Using Convolutional LSTM

This paper, a machine learning model for battlefield situation awareness is implemented using a model that utilizes the characteristics of both neural network models. The proposed neural network model is based on a Convolutional Neural Network(CNN)[1] model that extracts spatial feature and a Long Short-Term Memory(LSTM)[2] model, a type of Recurrent Neural Network(RNN) used to predict time series data. By using these two together, both temporal and spatial features of the data are learned to predict the battlefield situation.