Tensor RNN With Bayesian Nonparametric Mixture for Radar HRRP Modeling and Target Recognition

• Published in: IEEE transactions on signal processing Vol. 69; pp. 1995 - 2009
• Main Authors: Chen, Wenchao, Chen, Bo, Peng, Xiaojun, Liu, Jiaqi, Yang, Yang, Zhang, Hao, Liu, Hongwei
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayesian analysis; Clustering; Data models; Dependence; Dirichlet problem; Dirichlet process mixture; Feature extraction; Feature recognition; Gaussian mixture model (GMM); Hidden Markov models; high resolution range profile (HRRP); Indexes; Materials handling; Model testing; Modelling; Neural networks; Nonparametric statistics; online variational inference; Parameters; Probabilistic models; Radar; Radar automatic target recognition (RATR); Recurrent neural networks; stochastic gradient descent; Target recognition; tensor recurrent neural network (TRNN); Tensors
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2021.3065847

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.