Radio Source Localization using Sparse Signal Measurements from Uncrewed Ground Vehicles

Paper

Perera, Asanka, Tran, Vu Phi, Anavatti, Sreenatha, Kasmarik, Kathryn and Garratt, Matthew A.. 2023. "Radio Source Localization using Sparse Signal Measurements from Uncrewed Ground Vehicles." 2023 Australasian Conference on Robotics and Automation (ACRA 2023). Sydney, Australia 04 - 06 Dec 2023 Australia.
Paper/Presentation Title

Radio Source Localization using Sparse Signal Measurements from Uncrewed Ground Vehicles

Presentation TypePaper
AuthorsPerera, Asanka, Tran, Vu Phi, Anavatti, Sreenatha, Kasmarik, Kathryn and Garratt, Matthew A.
Number of Pages10
Year2023
Place of PublicationAustralia
Web Address (URL) of Conference Proceedingshttps://ssl.linklings.net/conferences/acra/acra2023_proceedings/views/at_a_glance.html
Conference/Event2023 Australasian Conference on Robotics and Automation (ACRA 2023)
Event Details
2023 Australasian Conference on Robotics and Automation (ACRA 2023)
Parent
Australasian Conference on Robotics and Automation
Delivery
In person
Event Date
04 to end of 06 Dec 2023
Event Location
Sydney, Australia
Event Venue
University of New South Wales
Abstract

Radio source localization can benefit many fields, including wireless communications, radar, radio astronomy, wireless sensor networks, positioning systems, and surveillance systems. However, accurately estimating the position of a radio transmitter using a remote sensor is not an easy task, as many factors contribute to the highly dynamic behavior of radio signals. In this study, we investigate techniques to use a mobile robot to explore an outdoor area and localize the radio source using sparse Received Signal Strength Indicator (RSSI) measurements. We propose a novel radio source localization method with fast turnaround times and reduced complexity compared to the state-of-theart. Our technique uses RSSI measurements collected while the robot completed a sparse trajectory using a coverage path planning map. The mean RSSI within each grid cell was used to find the most likely cell containing the source. Three techniques were analyzed with the data from eight field tests using a mobile robot. The proposed method can localize a gas source in a basketball field with a 1.2 m accuracy and within three minutes of convergence time, whereas the state-of-the-art active sensing technique took more than 30 minutes to reach a source estimation accuracy below 1 m.

Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204007. Control engineering, mechatronics and robotics
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Byline AffiliationsUniversity of New South Wales
University of Southern Queensland
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