Bidirectional recurrent gamma belief network for HRRP target recognition

Abstract

To characterize the bidirectional temporal dependence across the range cell of high resolution range profile (HRRP), we propose a bidirectional deep Poisson gamma dynamical system (bi-DPGDS) to extract features for target recognition. The proposed bi-DPGDS is a bidirectional dynamical deep probabilistic generative model, which builds temporal deep structure with a hierarchy of gamma distributions. For scalable training and fast out-of-sample prediction, we generalize bi-DPGDS to bidirectional recurrent gamma belief network (bi-rGBN) by incorporating a bidirectional recurrent variational inference network, which incorporates the temporal correlations from both directions into latent representation, and introduce a hybrid Bayesian inference scheme combining stochastic gradient Markov chain Monte Carlo (SG-MCMC) and amoritized variational inference. Moreover, we further propose an attention bi-rGBN (attn-bi-rGBN) for supervised learning. Experimental results on measured HRRP data demonstrate the effectiveness and efficiency of our model in classification and generalization, and its robustness to HRRP shift and data size.