Region-factorized recurrent attentional network with deep clustering for radar HRRP target recognition

Abstract

Feature extraction plays an essential role in radar automatic target recognition (RATR) with high-resolution range profiles (HRRPs). Traditional feature extraction algorithms usually ignore that different regions in HRRP contain the information with different importance, resulting in their inadequacy in characterizing HRRP data. In this work, we propose a region factorized recurrent attentional network (RFRAN) for HRRP-RATR by making use of the temporal dependence through recurrent neural network (RNN) and automatically finding the informative regions by a deep clustering mechanism in HRRP samples, which reflects the distribution of scatterers in target along range dimension. Specifically, we represent the temporal RNN hidden state using a region factorized encoder whose parameters are conditioned on the HRRP region cluster centers. Moreover an attention mechanism is used to weight up the different recognition contribution of each time step’s hidden state. The aim of all the above modules is to achieve a more informative and discriminative feature. Crucially, the loss function of RFRAN is differentiable, so all components can be jointly trained with a gradient-based optimization. Compared with traditional methods, besides the competitive recognition performance, RFRAN has a promising interpretability thanks to the sequential region-specific hidden states.