Airborne behaviour monitoring using Gaussian processes with map information
Hyondong Oh, Hyo‐Sang Shin, Seungkeun Kim, Antonios Tsourdos, Brian A. White · IET Radar Sonar & Navigation · 2013
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.