Enhanced Radar Imaging Using a Complex-valued Convolutional Neural Network

TL;DR

This paper introduces a complex-valued CNN framework tailored for radar imaging, demonstrating improved image quality and efficiency through simulations and experiments.

Contribution

It presents a novel complex-valued CNN approach for radar imaging, including modifications and training data generation methods, enhancing performance over existing techniques.

Findings

Superior imaging quality demonstrated in simulations and experiments

Enhanced computational efficiency compared to traditional methods

Effective adaptation of CNN for radar-specific imaging tasks

Abstract

Convolutional neural networks (CNN) have been successfully employed to tackle several remote sensing tasks such as image classification and show better performance than previous techniques. For the radar imaging community, a natural question is: Can CNN be introduced to radar imaging and enhance its performance? The presented letter gives an affirmative answer to this question. We firstly propose a processing framework by which a complex-valued CNN (CV-CNN) is used to enhance radar imaging. Then we introduce two modifications to the CV-CNN to adapt it to radar imaging tasks. Subsequently, the method to generate training data is shown and some implementation details are presented. Finally, simulations and experiments are carried out, and both results show the superiority of the proposed method on imaging quality and computational efficiency.