Bayesian Fusion of Unlabeled Vision and RF Data for Aerial Tracking of Ground Targets

• Published in: Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 1629 - 1636
• Main Authors: Rajasekaran, Ramya Kanlapuli, Ahmed, Nisar, Frew, Eric
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.10.2020
• Subjects: Cameras; Data models; Object detection; Radio frequency; Sensor fusion; Target tracking; Visualization
• ISSN: 2153-0866
• DOI: 10.1109/IROS45743.2020.9341410

This paper presents a method for target localization and tracking in clutter using Bayesian fusion of vision and Radio Frequency (RF) sensors used aboard a small Unmanned Aircraft System (sUAS). Sensor fusion is used to ensure tracking robustness and reliability in case of camera occlusion or RF signal interference. Camera data is processed using an off-the-shelf algorithm that detects possible objects of interest in a given image frame, and the true RF emitting target needs to be identified from among these if it is present. These data sources, as well as the unknown motion of the target, lead to a heavily non-linear non-Gaussian target state uncertainties, which are not amenable to typical data association methods for tracking. A probabilistic model is thus first rigorously developed to relate conditional dependencies between target movements, RF data and visual object detections. A modified particle filter is then developed to simultaneously reason over target states and RF emitter association hypothesis labels for visual object detections. Truth model simulations are presented to compare and validate the effectiveness of the RF + visual data fusion filter.