Cognitive Radar System for Obstacle Avoidance Using In-Motion Memory-Aided Mapping

Liyong Guo, Michail Antoniou, Christopher J. Baker

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


The paper introduces a radar signal processing method for goal-oriented, collision-free navigation in mobile robotic platforms. The derived algorithm creates an enhanced perception of the area in front of the sensor through accumulating a sequence of radar pulses that is constantly updated, and uses previously obtained perception to inform future robot steering actions on the fly, thus creating a form of working memory. The algorithm is analytically described, and experimentally confirmed in laboratory conditions with a ground mobile robot operating in real-time.

Original languageEnglish
Title of host publication2020 IEEE Radar Conference, RadarConf 2020
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)9781728189420
Publication statusPublished - 21 Sept 2020
Event2020 IEEE Radar Conference, RadarConf 2020 - Florence, Italy
Duration: 21 Sept 202025 Sept 2020

Publication series

NameIEEE National Radar Conference - Proceedings
ISSN (Print)1097-5659


Conference2020 IEEE Radar Conference, RadarConf 2020

Bibliographical note

Funding Information:
This work was supported in part by the National Key R&D Program of China (Grant No. 2018YFE0202101, 2018YFE0202102) and the China Scholarship Council. 978-1-7281-8942-0/20/$31.00 ©2020 IEEE

Publisher Copyright:
© 2020 IEEE.

Copyright 2020 Elsevier B.V., All rights reserved.


  • Cognitive radar
  • Collision avoidance
  • Map Memory
  • Vector Field Histogram

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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