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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/17840

Title: Airborne behaviour monitoring using Gaussian processes with map information
Authors: Oh, Hyondong
Shin, Hyo-Sang
Kim, Seungkeun
Tsourdos, Antonios
White, Barry A.
Issue Date: 2013
Publisher: © Institution of Engineering and Technology (IET)
Citation: OH, H. ... et al, 2013. Airborne behaviour monitoring using Gaussian processes with map information. IET Radar, Sonar and Navigation, 7 (4), pp. 393 - 400.
Abstract: This study proposes an airborne behaviour monitoring methodology of ground vehicles based on a statistical learning approach with domain knowledge given by road map information. To monitor and track the moving ground target using unmanned aerial vehicle aboard a moving target indicator, an interactive multiple model (IMM) filter is firstly applied. The IMM filter consists of an on-road moving mode using a road-constrained filter and an off-road moving mode using a conventional filter. Mode probability is also calculated from the IMM filter, and it provides deviation of the vehicle from the road. Then, a novel hybrid algorithm for anomalous behaviour recognition is developed using a Gaussian process regression on velocity profile along the one-dimensionalised position of the vehicle, as well as the deviation of the vehicle. To verify the feasibility and benefits of the proposed approach, a numerical simulation is performed using realistic car trajectory data in a city traffic.
Description: This paper is a postprint of a paper submitted to and accepted for publication in IET Radar, Sonar and Navigation and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.
Version: Accepted for publication
DOI: 10.1049/iet-rsn.2012.0255
URI: https://dspace.lboro.ac.uk/2134/17840
Publisher Link: http://dx.doi.org/10.1049/iet-rsn.2012.0255
ISSN: 1751-8784
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

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