Memory-augmented cognitive radar for obstacle avoidance using nearest steering vector search

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Memory-augmented cognitive radar for obstacle avoidance using nearest steering vector search. / Guo, Liyong; Antoniou, Michail; Baker, Chris.

In: IET Radar, Sonar and Navigation, Vol. 15, No. 1, 01.2021, p. 51-61.

Research output: Contribution to journalArticlepeer-review

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@article{2767c528527848ff8bbeb147552645f0,
title = "Memory-augmented cognitive radar for obstacle avoidance using nearest steering vector search",
abstract = "This study describes a cognitive radar architecture with application to real‐time obstacle avoidance in mobile robotic platforms. The concept of a world memory map is introduced as a means of providing an enhanced perception of the environment around the robotic platform. This is combined with a specially designed obstacle avoidance algorithm, Nearest Steering Vector Searching, all capable of operating in real‐time. The study analytically derives the radar signal processing algorithm, starting from range‐angle maps, so that a collision free course to a set destination point can be robustly navigated. Finally, the performance of this cognitive approach is examined through a number of proof‐of‐concept experiments using a commercial off‐the‐shelf radar mounted on a mobile ground robotic platform.",
author = "Liyong Guo and Michail Antoniou and Chris Baker",
year = "2021",
month = jan,
doi = "10.1049/rsn2.12012",
language = "English",
volume = "15",
pages = "51--61",
journal = "IET Radar, Sonar and Navigation",
issn = "1751-8784",
publisher = "Institution of Engineering and Technology (IET)",
number = "1",

}

RIS

TY - JOUR

T1 - Memory-augmented cognitive radar for obstacle avoidance using nearest steering vector search

AU - Guo, Liyong

AU - Antoniou, Michail

AU - Baker, Chris

PY - 2021/1

Y1 - 2021/1

N2 - This study describes a cognitive radar architecture with application to real‐time obstacle avoidance in mobile robotic platforms. The concept of a world memory map is introduced as a means of providing an enhanced perception of the environment around the robotic platform. This is combined with a specially designed obstacle avoidance algorithm, Nearest Steering Vector Searching, all capable of operating in real‐time. The study analytically derives the radar signal processing algorithm, starting from range‐angle maps, so that a collision free course to a set destination point can be robustly navigated. Finally, the performance of this cognitive approach is examined through a number of proof‐of‐concept experiments using a commercial off‐the‐shelf radar mounted on a mobile ground robotic platform.

AB - This study describes a cognitive radar architecture with application to real‐time obstacle avoidance in mobile robotic platforms. The concept of a world memory map is introduced as a means of providing an enhanced perception of the environment around the robotic platform. This is combined with a specially designed obstacle avoidance algorithm, Nearest Steering Vector Searching, all capable of operating in real‐time. The study analytically derives the radar signal processing algorithm, starting from range‐angle maps, so that a collision free course to a set destination point can be robustly navigated. Finally, the performance of this cognitive approach is examined through a number of proof‐of‐concept experiments using a commercial off‐the‐shelf radar mounted on a mobile ground robotic platform.

UR - http://digital-library.theiet.org/content/journals/iet-rsn

U2 - 10.1049/rsn2.12012

DO - 10.1049/rsn2.12012

M3 - Article

VL - 15

SP - 51

EP - 61

JO - IET Radar, Sonar and Navigation

JF - IET Radar, Sonar and Navigation

SN - 1751-8784

IS - 1

ER -