To deal with the temporal dependence between range cells in high resolution range profile (HRRP), dynamic methods, especially recurrent neural network (RNN), have been employed to extract features for target recognition. However, RNN has difficulty in complex and diverse sequence modeling problems as it ignores non-stationary sequential relationship between time-steps by sharing same parameters among all time-steps. Given this issue, we propose tensor recurrent neural network with Gaussian mixture model (GmTRNN) for HRRP, not only making use of temporal characteristic but also modeling the variation among its patterns. Specifically, a novel tensor RNN is developed by extending all the parameters in the form of tensor to explore diverse temporal dependence between range cells within an HRRP sample, where a mixture model is introduced to determine the parameters of each time-step in tensor RNN. Moreover, to take advantage of Bayesian nonparametrics in handling the unknown number of mixture components, we further propose the tensor recurrent neural network with Dirichlet process mixture (DPmTRNN). For scalable and joint training of clustering and recognition, we present effective hybrid online variational inference and stochastic gradient descent method. Experiments on benchmark data, measured and simulated HRRP data demonstrate the the effectiveness and efficiency of our models and its robustness to HRRP shift.

中文翻译：

---

具有贝叶斯非参数混合的Tensor RNN用于雷达HRRP建模和目标识别

为了处理高分辨率距离轮廓（HRRP）中距离单元之间的时间依赖性，已采用动态方法，尤其是递归神经网络（RNN）来提取目标识别的特征。但是，RNN难以解决复杂多样的序列建模问题，因为它通过在所有时间步之间共享相同的参数来忽略时间步之间的非平稳顺序关系。鉴于这个问题，我们提出了具有高斯混合模型（GmTRNN）的HRRP张量递归神经网络，不仅利用时间特性，而且还对其模式之间的变化进行建模。具体而言，通过以张量的形式扩展所有参数来开发新的张量RNN，以探索HRRP样本内范围单元之间的不同时间依赖性，引入混合模型以确定张量RNN中每个时间步长的参数。此外，为了利用贝叶斯非参数处理未知数量的混合物组分，我们进一步提出了带有Dirichlet过程混合物（DPmTRNN）的张量递归神经网络。为了对聚类和识别进行可扩展的联合训练，我们提出了有效的混合在线变分推理和随机梯度下降方法。在基准数据，测量和模拟的HRRP数据上进行的实验证明了我们模型的有效性和效率，以及其对HRRP偏移的鲁棒性。我们进一步提出了具有Dirichlet过程混合（DPmTRNN）的张量递归神经网络。为了对聚类和识别进行可扩展的联合训练，我们提出了有效的混合在线变分推理和随机梯度下降方法。在基准数据，测量和模拟的HRRP数据上进行的实验证明了我们模型的有效性和效率，以及其对HRRP偏移的鲁棒性。我们进一步提出了具有Dirichlet过程混合（DPmTRNN）的张量递归神经网络。为了对聚类和识别进行可扩展的联合训练，我们提出了有效的混合在线变分推理和随机梯度下降方法。在基准数据，测量和模拟的HRRP数据上进行的实验证明了我们模型的有效性和效率，以及其对HRRP偏移的鲁棒性。