On Self-Localization and Tracking with an Unknown Number of Targets

We propose a scalable algorithm for cooperative self-localization and multi-target tracking. Mobile agents localize themselves, and track an unknown number of targets in the presence of data-association uncertainty. We accomplish this task by modeling the probabilistic relationship between agent and target measurements alongside their state estimates using a suitably designed factor graph, which is solved using loopy belief propagation. By coupling the self-localization and multi-target tracking approaches together, our approach is capable of leveraging the statistical correlation between agent and target state uncertainty in order to provide improved localization accuracy over time for both agents and targets. Simulation results show improvement in target and agent state estimation error, compared to the conventional approach of first localizing agents, and then using this information for target tracking.