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Support vector regression based sensor localization using UAV

Das, K and Ghose, D and Lima, R (2019) Support vector regression based sensor localization using UAV. In: 34th Annual ACM Symposium on Applied Computing, SAC 2019, 8 - 12 April 2019, Limassol, pp. 938-945.

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Official URL: https://doi.org/10.1145/3297280.3297373


In this work, we focus on localization of a beacon which is a source of radio frequency (RF) signal using a single unmanned aerial vehicle (UAV). We propose to use the Support Vector Regression (SVR) technique to directly localize the sensor by using the received signal strength (RSS) of the RF signal as the input. A systematic method to collect the samples of the RSS values followed by the filtering techniques to process the noise in the RSS measurements as well as the estimate of the sensor position are presented. Pure pursuit guidance law is used to guide the UAV to the estimated sensor location. The algorithm is tested by means of simulations, where it was shown to estimate the position within 2 m accuracy.

Item Type: Conference Paper
Publication: Proceedings of the ACM Symposium on Applied Computing
Publisher: Association for Computing Machinery
Additional Information: The copyright for this article belongs to Association for Computing Machinery.
Keywords: Antennas; Regression analysis, Filtering technique; Localization; Pure pursuit guidance; Radiofrequency signals; Received signal strength; Sensor localization; Support vector regression (SVR); Systematic method, Unmanned aerial vehicles (UAV)
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 22 Nov 2022 09:49
Last Modified: 22 Nov 2022 09:49
URI: https://eprints.iisc.ac.in/id/eprint/77966

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