论文标题
雷达HRRP目标识别的变性时间深生成模型
Variational Temporal Deep Generative Model for Radar HRRP Target Recognition
论文作者
论文摘要
我们基于高分辨率范围（HRRP）开发了用于雷达自动识别（RGBN）的复发性伽马信念网络（RGBN），该目标识别（HRRP）表征了HRRP范围内的时间依赖性。拟议的RGBN采用了伽马分布的层次结构，以建立其时间深的生成模型。为了进行可扩展的训练和快速的样本预测，我们提出了随机毕业者马尔可夫链蒙特卡洛（MCMC）的混合体和一个经常性的变异推理模型来执行后推断。为了利用标签信息来提取更多的歧视性潜在表示，我们进一步提出了监督的卢比，以共同对HRRP样本及其相应的标签进行建模。合成和测量的HRRP数据的实验结果表明，所提出的模型在计算方面具有良好的分类精度和概括能力，并提供高度可解释的多层层状潜在潜在结构。
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.
