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.

中文翻译：

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雷达 HRRP 目标识别的变分时间深度生成模型

我们基于高分辨率距离剖面 (HRRP) 开发了用于雷达自动目标识别 (RATR) 的循环伽马信念网络 (rGBN)，该网络表征了 HRRP 范围单元之间的时间依赖性。所提出的 rGBN 采用伽马分布的层次结构来构建其时间深度生成模型。对于可扩展的训练和快速的样本外预测，我们提出了随机梯度马尔可夫链蒙特卡罗 (MCMC) 和循环变分推理模型的混合，以执行后验推理。为了利用标签信息提取更具辨别力的潜在表示，我们进一步提出了有监督的 rGBN 来联合建模 HRRP 样本及其相应的标签。合成和测量 HRRP 数据的实验结果表明，所提出的模型计算效率高，