Radar HRRP target recognition with deep networks
作者：
Highlights：
• An HRRP ATR procedure based on deep networks is developed to replace the shallow algorithms.
• Stacked Corrective Autoencoders (SCAE) is further proposed for HRRP ATR considering HRRP's characteristics.
• Experiments based on measured HRRP data show the SCAE outperforms several feature extraction methods.
• Some detailed experiments further validate the generalization performance of SCAE with the limit training data.
摘要
•An HRRP ATR procedure based on deep networks is developed to replace the shallow algorithms.•Stacked Corrective Autoencoders (SCAE) is further proposed for HRRP ATR considering HRRP's characteristics.•Experiments based on measured HRRP data show the SCAE outperforms several feature extraction methods.•Some detailed experiments further validate the generalization performance of SCAE with the limit training data.
论文关键词：Radar automatic target recognition (RATR),High-resolution range profile (HRRP),Deep networks,Stacked Corrective Autoencoders (SCAE)
论文评审过程：Received 8 August 2014, Revised 12 June 2016, Accepted 15 August 2016, Available online 16 August 2016, Version of Record 25 August 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.08.012
