Convolutional neural networks for radar HRRP target recognition and rejection

Jinwei Wan,Bo Chen,Lin Jin,Bin Xu,Hongwei Liu

doi:10.1186/s13634-019-0603-y

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.

Highlights

Radar target detection and radar automatic target recognition (RATR) are two active research fields of modern radar technology
2 Preliminaries we briefly review the concepts of high-resolution range profile (HRRP) and Convolutional neural network (CNN), following which the corresponding descriptions of time domain and the spectrogram feature of HRRP are provided
Let xS ∈ RH×W×1 denote the spectrogram feature of HRRP, where H and W represent the dimensions of frequency and time domain of spectrogram, Fig. 4 Illustration of a convolutional layer in CNN

Summary

Introduction

Radar target detection and radar automatic target recognition (RATR) are two active research fields of modern radar technology. Feng et al [19] employ the average profile as the correction terms and stack a series of Corrective Autoencoders to extract features from HRRP These two models are based on fully connected nets and may not capture the structural information among the range cells of HRRP layer by layer, since HRRP reflects the distribution of scatterers in target along the range dimension. We first develop a one-dimensional CNN recognition procedure for the time domain HRRP, which represents the amplitude of projection of the target scattering centers onto the radar LOS and is widely used in RATR.

Time domain HRRP

HRRP target recognition and outlier rejection based on CNN

Batch normalization

Results and discussion

Method

Conclusion