Convolutional neural networks for radar HRRP target recognition and rejection

Abstract

Robust and efficient feature extraction is critical for high-resolution range profile (HRRP)-based radar automatic target recognition (RATR). In order to explore the correlation between range cells and extract the structured discriminative features in HRRP, in this paper, we take advantage of the attractive properties of convolutional neural networks (CNNs) to address HRRP RATR and rejection problem. Compared with the time domain representations, the spectrogram of HRRP records the amplitude feature and characterizes the phase information among the range cells. Thus, besides using one-dimensional CNN to handle HRRP in time domain, we also devise a two-dimensional CNN model for the spectrogram feature. Furthermore, by adding a deconvolutional decoder, we integrate the target recognition with outlier rejection task together. Experimental results on measured HRRP data show that our CNN model outperforms many state-of-the-art methods for both recognition and rejection tasks.