Using Long Short Term Memory (LSTM) For Radar Cross Section Prediction

Radar Cross Section (RCS) is a measurement of scattering performance of an object. RCS plays an important role in the design of stealth weapon system. However, the method of calculating RCS of every angle of azimuth and elevation is complex, and the efficiency is low. Evidently, it is of great significance for predicting the unknown RCS given a set of RCS data of an object. Towards this aim, we propose a novel approach using multilayers Long Short Term Memory (LSTM) networks based on unsupervised learning - the mechanism is similar to that of autoencoder. The encoder LSTM maps the input RCS into a fixed length representation. The decoder LSTM decodes the representation to predict RCS. For the purpose of this work, we create an 3D object and collect its RCS for a dataset with the help of electromagnetic simulation software, FEKO. The proposed networks have been applied on the test dataset and yield satisfactory results.