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

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

Standard

Cognitive Radar System for Obstacle Avoidance Using In-Motion Memory-Aided Mapping. / Guo, Liyong; Antoniou, Michail; Baker, Christopher J.

2020 IEEE Radar Conference, RadarConf 2020. Institute of Electrical and Electronics Engineers (IEEE), 2020. 9266655 (IEEE National Radar Conference - Proceedings; Vol. 2020-September).

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

Harvard

Guo, L, Antoniou, M & Baker, CJ 2020, Cognitive Radar System for Obstacle Avoidance Using In-Motion Memory-Aided Mapping. in 2020 IEEE Radar Conference, RadarConf 2020., 9266655, IEEE National Radar Conference - Proceedings, vol. 2020-September, Institute of Electrical and Electronics Engineers (IEEE), 2020 IEEE Radar Conference, RadarConf 2020, Florence, Italy, 21/09/20. https://doi.org/10.1109/RadarConf2043947.2020.9266655

APA

Guo, L., Antoniou, M., & Baker, C. J. (2020). Cognitive Radar System for Obstacle Avoidance Using In-Motion Memory-Aided Mapping. In 2020 IEEE Radar Conference, RadarConf 2020 [9266655] (IEEE National Radar Conference - Proceedings; Vol. 2020-September). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/RadarConf2043947.2020.9266655

Vancouver

Guo L, Antoniou M, Baker CJ. Cognitive Radar System for Obstacle Avoidance Using In-Motion Memory-Aided Mapping. In 2020 IEEE Radar Conference, RadarConf 2020. Institute of Electrical and Electronics Engineers (IEEE). 2020. 9266655. (IEEE National Radar Conference - Proceedings). https://doi.org/10.1109/RadarConf2043947.2020.9266655

Author

Guo, Liyong ; Antoniou, Michail ; Baker, Christopher J. / Cognitive Radar System for Obstacle Avoidance Using In-Motion Memory-Aided Mapping. 2020 IEEE Radar Conference, RadarConf 2020. Institute of Electrical and Electronics Engineers (IEEE), 2020. (IEEE National Radar Conference - Proceedings).

Bibtex

@inproceedings{ee4e14817b2c4fe598ea165ede7af987,
title = "Cognitive Radar System for Obstacle Avoidance Using In-Motion Memory-Aided Mapping",
abstract = "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. ",
keywords = "Cognitive radar, Collision avoidance, Map Memory, Vector Field Histogram",
author = "Liyong Guo and Michail Antoniou and Baker, {Christopher J.}",
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 {\textcopyright}2020 IEEE Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 IEEE Radar Conference, RadarConf 2020 ; Conference date: 21-09-2020 Through 25-09-2020",
year = "2020",
month = sep,
day = "21",
doi = "10.1109/RadarConf2043947.2020.9266655",
language = "English",
series = "IEEE National Radar Conference - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
booktitle = "2020 IEEE Radar Conference, RadarConf 2020",

}

RIS

TY - GEN

T1 - Cognitive Radar System for Obstacle Avoidance Using In-Motion Memory-Aided Mapping

AU - Guo, Liyong

AU - Antoniou, Michail

AU - Baker, Christopher J.

N1 - 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: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/9/21

Y1 - 2020/9/21

N2 - 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.

AB - 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.

KW - Cognitive radar

KW - Collision avoidance

KW - Map Memory

KW - Vector Field Histogram

UR - http://www.scopus.com/inward/record.url?scp=85098554030&partnerID=8YFLogxK

U2 - 10.1109/RadarConf2043947.2020.9266655

DO - 10.1109/RadarConf2043947.2020.9266655

M3 - Conference contribution

AN - SCOPUS:85098554030

T3 - IEEE National Radar Conference - Proceedings

BT - 2020 IEEE Radar Conference, RadarConf 2020

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2020 IEEE Radar Conference, RadarConf 2020

Y2 - 21 September 2020 through 25 September 2020

ER -