Particle Filtering-Based In-Flight Icing Detection for Unmanned Aerial Vehicles

Ice accretion poses a threat to fixed-wing aerial vehicles as it alters the wings' shape and thus degrades the aerodynamic performance. In manned aircraft, the icing detection system assists the pilot and utilises dedicated sensors. However, in unmanned aerial vehicles (UAVs), onboard icing detection can generally only be achieved using standard sensors in conjunction with dynamical models, because dedicated sensors are rarely available. In this paper, we propose two approaches based on the particle filter for both icing detection and accurate state and aerodynamic parameter estimation in the presence of icing, with different levels of severity. The first approach uses the observation likelihood for icing hypothesis testing with a complement of the Gaussian kernel to compute icing probability. The second approach uses a discrete jump approach based on a Bernoulli process and a subset of particles to test the icing hypothesis for faster icing detection by estimating changes in icing-related aerodynamic parameters. Using both approaches, the simulation results demonstrate improved estimation accuracy compared to an extended Kalman filter (EKF), under both moderate and severe icing conditions. With adequate tuning, the proposed approaches show potential for indirect icing detection in UAVs. They also enable the computation of icing severity and provide a more accurate and reliable estimate of the icing probability compared to the EKF.