Unmanned Aerial Vehicles (UAVs) localization has become crucial in recent years, mainly for navigation or self-positioning and for UAV based security monitoring and surveillance. In this paper, azimuth and elevation radio positioning of UAVs are considered. The localization is based on multiple differential phase-of-arrival measures exploiting a 3-Axial Uniform Linear Array of antennas. An ad hoc particle filtering algorithm is applied to improve the positioning performance using a dynamic motion model. A novel adaptive algorithm, namely, Particles Swarm Adaptive Scattering (PSAS), is proposed to increment the algorithm stability and precision. To assess performance a Confined Area Random Aerial Trajectory Emulator (CARATE) algorithm has been developed to generate actual paths of flying UAVs. The algorithm performance is compared with the baseline method and with the average trajectory Cramér Rao lower bound to show the effectiveness of the proposed algorithm.

Azimuth and Elevation Dynamic Tracking of UAVs via 3-Axial ULA and Particle Filtering

PAPAIZ, Andrea;TONELLO, Andrea
2016

Abstract

Unmanned Aerial Vehicles (UAVs) localization has become crucial in recent years, mainly for navigation or self-positioning and for UAV based security monitoring and surveillance. In this paper, azimuth and elevation radio positioning of UAVs are considered. The localization is based on multiple differential phase-of-arrival measures exploiting a 3-Axial Uniform Linear Array of antennas. An ad hoc particle filtering algorithm is applied to improve the positioning performance using a dynamic motion model. A novel adaptive algorithm, namely, Particles Swarm Adaptive Scattering (PSAS), is proposed to increment the algorithm stability and precision. To assess performance a Confined Area Random Aerial Trajectory Emulator (CARATE) algorithm has been developed to generate actual paths of flying UAVs. The algorithm performance is compared with the baseline method and with the average trajectory Cramér Rao lower bound to show the effectiveness of the proposed algorithm.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11390/1098395
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