Recurrent neural networks for discrimination of exo-atmospheric targets based on infrared radiation signature

Abstract

Exo-atmospheric infrared (IR) target discrimination is an important research problem in space attack and defense. The different micro-motion states of the targets result in respective characteristics in the obtained IR radiation intensity sequences, and this difference is difficult to describe intuitively and extract effectively. Few methods can effectively contact the data with the micro-motion model, resulting in a low classification accuracy and difficult to meeting actual application requirements. We set up four types of targets by constructing the micro-motion model of the exo-atmospheric targets, including the warhead and heavy decoy, with spinning and coning motion; and the light decoy and debris, with tumbling motion, to get the IR radiation intensity sequences. We use random projection to improve the discrimination power of recurrent neural network, and to classify the time series of IR radiation intensity. Experimental results demonstrate that random projection recurrent neural network (R-RNN) is more effective than several other typical algorithms in time series classification (TSC) task, which can achieve an excellent target discrimination. We also analyze the effect of noise on the performance of the algorithm.