Variational Temporal Deep Generative Model for Radar HRRP Target Recognition

• URL: http://arxiv.org/abs/2009.13011v1
• Date: Mon, 28 Sep 2020 02:03:51 GMT
• Title: Variational Temporal Deep Generative Model for Radar HRRP Target Recognition
• Authors: Dandan Guo, Bo Chen (Senior Member, IEEE), Wenchao Chen, Chaojie Wang, Hongwei Liu (Member, IEEE), and Mingyuan Zhou
• Abstract summary: We develop a recurrent gamma belief network (rGBN) for radar automatic target recognition (RATR) based on high-resolution range profile (HRRP) The proposed rGBN adopts a hierarchy of gamma distributions to build its temporal deep generative model. Experimental results on synthetic and measured HRRP data show that the proposed models are efficient in computation, have good classification accuracy and ability, and provide highly interpretable multi-stochastic-layer latent structure.
• Score: 39.01318281591659
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We develop a recurrent gamma belief network (rGBN) for radar automatic target recognition (RATR) based on high-resolution range profile (HRRP), which characterizes the temporal dependence across the range cells of HRRP. The proposed rGBN adopts a hierarchy of gamma distributions to build its temporal deep generative model. For scalable training and fast out-of-sample prediction, we propose the hybrid of a stochastic-gradient Markov chain Monte Carlo (MCMC) and a recurrent variational inference model to perform posterior inference. To utilize the label information to extract more discriminative latent representations, we further propose supervised rGBN to jointly model the HRRP samples and their corresponding labels. Experimental results on synthetic and measured HRRP data show that the proposed models are efficient in computation, have good classification accuracy and generalization ability, and provide highly interpretable multi-stochastic-layer latent structure.

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