Oh, HyondongShin, HyosangKim, SeungkeunTsourdos, AntoniosWhite, Brian A.2014-01-232014-01-232013-07-31Hyondong 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 – 400.1751-8784http://dx.doi.org/10.1049/iet-rsn.2012.0255http://dspace.lib.cranfield.ac.uk/handle/1826/8093This 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-UKThis 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."Article