Human Micro-Doppler Signature Classification in the Presence of a Selection of Jamming Signals

被引：0

作者：

Dhulashia, Dilan ^{[1
]}

Ritchie, Matthew ^{[1
]}

Vishwakarma, Shelly ^{[2
]}

Chetty, Kevin ^{[2
]}

机构：

[1] UCL, Dept Elect & Elect Engn, London, England

[2] UCL, Dept Secur & Crime Sci, London, England

来源：

2021 IEEE RADAR CONFERENCE (RADARCONF21): RADAR ON THE MOVE | 2021年

基金：

英国工程与自然科学研究理事会;

关键词：

micro-Doppler; synthetic jamming; radar classification; radar machine learning; RADAR;

D O I：

10.1109/RadaraConf2147009.2021.9455278

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This work investigates the degradation effects of four distinct jamming signal styles on human micro-Doppler signatures by examining the ability of a linear discriminant classifier to accurately distinguish signatures collected using a simulated frequency modulated continuous wave (FMCW) radar which have been injected with jamming. Misclassification dependence on jamming signal power for each jamming style is presented along with the nature of misclassifications.

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收藏

页数：6

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