Airborne behaviour monitoring using Gaussian processes with map information

Show simple item record Oh, Hyondong - Shin, Hyo-Sang - Kim, Seungkeun - Tsourdos, Antonios - White, Brian A. - 2014-01-23T05:01:43Z 2014-01-23T05:01:43Z 2013-07-31T00:00:00Z -
dc.identifier.citation Hyondong Oh, Hyo-Sang Shin, Seungkeun Kim, Antonios Tsourdos, Brian A. White, Airborne behaviour monitoring using Gaussian processes with map information, IET Radar, Sonar & Navigation, Volume 7, Issue 4, April 2013, Pages 393 – 400.
dc.identifier.issn 1751-8784 -
dc.identifier.uri -
dc.description.abstract This paper 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 UAVs aboard a moving target indicator, an interactive multiple model (IMM) filter is firstly applied. {\color{red}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. en_UK
dc.language.iso en_UK -
dc.publisher Institution of Engineering and Technology en_UK
dc.rights This paper is a postprint of a paper submitted to and accepted for publication in IET Radar, Sonar & Navigation and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library."
dc.title Airborne behaviour monitoring using Gaussian processes with map information en_UK
dc.type Article -

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