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Variational Temporal Deep Generative Model for Radar HRRP Target Recognition by Guo, Dandan; Chen, Bo; Chen, Wenchao; Wang, Chaojie; Liu, Hongwei; Zhou, Mingyuan is a Computer Science article available to read on EtoBox.

What is Variational Temporal Deep Generative Model for Radar HRRP Target Recognition about?

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-ofsample 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 multistochastic-layer latent structure.

Who reads Variational Temporal Deep Generative Model for Radar HRRP Target Recognition?

Author

Guo, Dandan; Chen, Bo; Chen, Wenchao; Wang, Chaojie; Liu, Hongwei; Zhou, Mingyuan

Publisher

IEEE; Institute of Electrical and Electronics Engineers; Institute of Electrical and Electronics Engineers (IEEE) (ISSN 1053-587X)

Published

2020

Language

DA

Field

Computer Science (Physical Sciences)