A nanodrone swarm is formulated by multiple lightweight and low-cost nanodrones to perform tasks in very challenging environments. Therefore, it is essential to recognize the relative positions of the nanodrones in the swarm for accurate and safe platooning. However, the camera or LiDAR sensors suffer from the line-of-sight (LoS) sensing. This paper presents the design, implementation, and evaluation of RFDrone, a system that can enable the relative positioning for the nanodrone swarm using the commodity passive RFID tags attached to each nanodrone for safe and accurate platooning. To do so, we propose a novel algorithm for spatial-temporal phase profiling based on the swarm platooning nature and further design a machine-learning model to predict the relative position of the tagged nanodrones in the swarm. Our experiments show that RFDrone can accurately estimate the relative positions of nanodrones in the swarm, whereby the 3D geometry of the nanodrone swarm pattern is easily obtained across different environments, tag diversity, and swarm pattern diversity.

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