Accurate Detection and Localization of Unmanned Aerial Vehicle Swarms-Enabled Mobile Edge Computing System

Unmanned aerial vehicle (UAV) swarms-enabled mobile edge computing system can be deployed in critical industrial zones for monitoring. Meanwhile, its malicious use may bring great threat to the security, and the accurate detection, and localization are important. UAV swarms show characteristics of the high density, small radar cross section, far range, and time-varying motion, and have posed formidable challenges to the accurate detection and localization. In this article, the accurate detection and localization of UAV swarms are investigated, and an effective method is proposed based on the Dechirp-keystone transform, and frequency-selective reweighted trace minimization. It inherits high robustness of the coherent long-time integration technique and superresolution of the gridless sparse technique. Mathematical analyzes and numerical simulations validate its superiorities in accurate detection and localization of UAV swarms.

I. Introduction

Mobile edge computing (MEC) provides computing, storage, and other infrastructure close to data sources or users, and provides cloud services and IT environment services for edge applications [1], [2]. Compared with the centralized cloud computing service, MEC solves the problems of long delay and excessive aggregated traffic, and provides better support for real-time and bandwidth-intensive services. Thus, MEC can be implemented in critical industrial zone for monitoring. In some special tasks, the dispatch of workers into dangerous zones should be avoided [3]–[5] and the unmanned aerial vehicle (UAV)-enabled MEC system is a good choice.