Radar Accurate Localization of UAV Swarms Based on Range Super-Resolution Method

TL;DR

This paper introduces a super-resolution radar localization framework for UAV swarms that improves accuracy and robustness without hardware changes, validated through simulations and real experiments.

Contribution

It proposes a novel range super-resolution method based on gridless sparse techniques and long-time integration, enhancing UAV swarm localization accuracy.

Findings

Range super-resolution method outperforms KT, MUSIC, and RAM methods in noisy environments.

The method is robust and practical for real-world UAV swarm localization.

Experimental results confirm effectiveness with X-band radar.

Abstract

In radar accurate localization of unmanned aerial vehicle (UAV) swarms, the high density, similar motion parameters, small radar cross-section (RCS), strong noise and far range put forward high requirements on radar resolution and transmitting power. In this paper, by using advantages of the long-time integration (LTI) technique and gridless sparse method, we construct a super-resolution framework for radar accurate localization of UAV swarms without changing radar hardware and system parameters. Thereafter, based on this framework, a range super-resolution method is proposed to realize the radar accurate localization of UAV swarms. Mathematical analyses and numerical simulations are performed and demonstrate that, compared to the keystone transform (KT)-based LTI method, MUSIC-based method and reweighted atomic-norm minimization (RAM)-based method, the range super-resolution method is more robust and practical for radar accurate localization of UAV swarms under the noisy environment. Additionally, the real experiment with X-band radar is also conducted to verify the effectiveness of the range super-resolution method.