A high-speed multi-purpose software defined radar for near-field applications

Wenda Li, Chong Tang, Shelly Vishwakarma, Fangzhan Shi, Robert Piechocki, Kevin Chetty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Software Defined Radar (SDRadar) is a unique radar system, where most of its processing, like filtering, correlation, signal generation etc. is performed by software. This means SDRadar can be flexibly deployed for different purposes and with a relative short development process. In this paper, we present a generic SDRadar system that can operate in different setups for near-field monitoring applications. Practical solutions for traditional limitations in SDRadar and high sampling rates are introduced, and its performance is demonstrated using a commercial universal software radio peripheral (USRP) device with four synchronized receiving channels and a maximum sampling rate of 100MHz. Additionally, a GPU accelerator has been implemented to deal with the high sampling rate. Three different setups have been tested to demonstrate the feasibility of the propose SDRadar system with distributed nodes, vertically positioned nodes and a miniature scenario. Recorded Doppler signatures have shown the proposed SDRadar can effectively capture the body and hand gestures. Such results can be used in a range of applications such as eHealth, human-machine interaction and indoor tracking.
Original languageEnglish
Title of host publicationIEEE Radar Conference 2022
Subtitle of host publicationConference Proceedings
Place of PublicationNew York City, NY
Number of pages6
ISBN (Electronic)9781728153681
ISBN (Print)9781728153698
Publication statusPublished - 21 Mar 2022
Event2022 IEEE Radar Conference - Sheraton New York Times Square, New York, United States
Duration: 21 Mar 202225 Mar 2022


Conference2022 IEEE Radar Conference
Abbreviated titleRadarConf22
Country/TerritoryUnited States
CityNew York
Internet address


  • Software Defined Radar
  • Doppler signature
  • multichannel
  • Real-Time
  • GPU accelerator


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