Drone Detection via Low Complexity Zadoff-Chu Sequence Root Estimation

2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2020-01-01 DOI:10.1109/CCNC46108.2020.9045243

Chun Kin Au-Yeung, Brandon F. Lo, Scott Torborg

{"title":"Drone Detection via Low Complexity Zadoff-Chu Sequence Root Estimation","authors":"Chun Kin Au-Yeung, Brandon F. Lo, Scott Torborg","doi":"10.1109/CCNC46108.2020.9045243","DOIUrl":null,"url":null,"abstract":"The fast growth of drone applications in industrial, commercial and consumer domains in recent years has caused great security, safety and privacy concerns. For this reason, a demand for solutions of drone detection and monitoring in consumer market can be foreseen in the near future. For detecting drones that employ Zadoff-Chu (ZC) sequences for synchronization, these ZC sequences can be used to determine their presence in the air. However, it is a great challenge to blindly detect thousands of unknown ZC sequences possibly used by the drones in real time. Existing solutions mainly developed for LTE systems cannot be directly applied to the blind detection of ZCs in drones without incurring huge cost. In this paper, a low-complexity cost effective blind ZC detection method based on double differential is proposed for low-cost drone detection and monitoring systems. Signal-to-noise ratio (SNR) lower bounds are analytically derived for low and high SNR regimes. Monte Carlo simulations show the proposed scheme performs well in moderate and high SNR.","PeriodicalId":443862,"journal":{"name":"2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCNC46108.2020.9045243","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

Abstract

The fast growth of drone applications in industrial, commercial and consumer domains in recent years has caused great security, safety and privacy concerns. For this reason, a demand for solutions of drone detection and monitoring in consumer market can be foreseen in the near future. For detecting drones that employ Zadoff-Chu (ZC) sequences for synchronization, these ZC sequences can be used to determine their presence in the air. However, it is a great challenge to blindly detect thousands of unknown ZC sequences possibly used by the drones in real time. Existing solutions mainly developed for LTE systems cannot be directly applied to the blind detection of ZCs in drones without incurring huge cost. In this paper, a low-complexity cost effective blind ZC detection method based on double differential is proposed for low-cost drone detection and monitoring systems. Signal-to-noise ratio (SNR) lower bounds are analytically derived for low and high SNR regimes. Monte Carlo simulations show the proposed scheme performs well in moderate and high SNR.

查看原文本刊更多论文

基于低复杂度Zadoff-Chu序列根估计的无人机检测

近年来，无人机在工业、商业和消费领域的应用快速增长，引起了人们对安全、安全和隐私的极大关注。因此，在不久的将来，可以预见消费市场对无人机检测和监控解决方案的需求。对于检测采用Zadoff-Chu (ZC)序列进行同步的无人机，这些ZC序列可用于确定它们在空中的存在。然而，对无人机可能使用的数千个未知ZC序列进行实时盲检测是一个巨大的挑战。现有主要针对LTE系统开发的解决方案，在不产生巨大成本的情况下，无法直接应用于无人机中zc的盲检测。本文针对低成本无人机检测与监控系统，提出了一种基于双差分的低复杂度、高性价比的盲ZC检测方法。分析了低信噪比和高信噪比的下边界。蒙特卡罗仿真结果表明，该方案在中高信噪比下均具有良好的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)

自引率

0.00%

发文量