Radio Source Localization using Sparse Signal Measurements from Uncrewed Ground Vehicles
Paper
Paper/Presentation Title | Radio Source Localization using Sparse Signal Measurements from Uncrewed Ground Vehicles |
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Presentation Type | Paper |
Authors | Perera, Asanka, Tran, Vu Phi, Anavatti, Sreenatha, Kasmarik, Kathryn and Garratt, Matthew A. |
Number of Pages | 10 |
Year | 2023 |
Place of Publication | Australia |
Web Address (URL) of Conference Proceedings | https://ssl.linklings.net/conferences/acra/acra2023_proceedings/views/at_a_glance.html |
Conference/Event | 2023 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 Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 4007. Control engineering, mechatronics and robotics |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of New South Wales |
University of Southern Queensland |
https://research.usq.edu.au/item/z77w9/radio-source-localization-using-sparse-signal-measurements-from-uncrewed-ground-vehicles
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