Sparse Bayesian Learning for Quadcopter Localisation

Over the past years, the use of acoustics for detection and localisation of hazardous drones has increased. A large number of studies focusing on this work use beamforming for this application. The aim of the research presented here is to investigate if a different technique can be used to improve the localisation range which can be achieved by beamforming. The technique used in this work is sparse Bayesian learning. Beamforming relies on a minimisation between measured and modelled data. Opposite to this, sparse Bayesian learning is a probabilistic parameter estimation technique. Both techniques are applied to real-world measurement data of fly-overs with 5 different quadcopters. The azimuth and elevation angles of the drones are estimated and the time interval in seconds is determined during which the drones are localised continuously. For both angles, sparse Bayesian learning clearly results in longer intervals. However, the exact improvement varies per drone. In azimuth direction, the intervals were 4.2% to 59.2% longer. In elevation direction, this was 1.2% to 40.4%.