Convolutional factor analysis model with application to radar automatic target recognition
作者：
Highlights：
• A convolutional factor analysis model is developed and applied to RATR.
• The dictionary in our model could capture the basic structures of observed data.
• Our model has fewer parameters and can be learned better with fewer training data.
• Efficient inference is performed via VB method for our hierarchical Bayesian model.
• Experiments for real radar data show our method outperforms other related methods.
摘要
•A convolutional factor analysis model is developed and applied to RATR.•The dictionary in our model could capture the basic structures of observed data.•Our model has fewer parameters and can be learned better with fewer training data.•Efficient inference is performed via VB method for our hierarchical Bayesian model.•Experiments for real radar data show our method outperforms other related methods.
论文关键词：Convolutional factor analysis (CFA),Dictionary learning,Radar automatic target recognition (RATR),High-resolution range profile (HRRP),Variational Bayesian (VB)
论文评审过程：Received 26 June 2017, Revised 21 August 2018, Accepted 9 October 2018, Available online 11 October 2018, Version of Record 19 October 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.10.014
