Self-supervised Enhanced Radar Imaging Based on Deep-Learning-Assisted Compressed Sensing

TL;DR

This paper introduces a self-supervised deep learning method that enhances radar imaging quality by leveraging compressed sensing and sparsity, achieving better resolution and noise suppression with limited training data.

Contribution

The proposed SS-DL-CS-Net combines self-supervised learning with compressed sensing to improve radar image quality without requiring extensive labeled datasets.

Findings

Outperforms traditional radar imaging methods in resolution

Demonstrates superior noise suppression capabilities

Effective with limited training data

Abstract

Traditional radar imaging methods suffer from the problems of low resolution and poor noise suppression. We propose a new radar imaging method based on Self-supervised deep-learning-assisted compressed sensing (SS-DL-CS-Net). The original radar image as the input of net. The net is trained to learn the mapping function between the original radar image and the high quality radar image. However, the high quality radar image cant be obtained. We solve this problem by used the sparsity of radar image. The original radar image and image with the zeros value as the reference of net. Ours net dont need a lot of data to train. Real radar data are used to evaluate the performance of the proposed method. The experimental results demonstrate the superiority of the proposed method