A Recursive Distributed Framework for Joint Localization and Target Tracking with Asynchronous Pairwise Communication

Distributed joint localization and target tracking is crucial for various applications. The major challenge lies in accurately estimating inter-robot and robot-target cross-correlations, particularly under intermittent or unreliable communication. Existing approaches suffer from several limitations, including the decoupled treatment of localization and target tracking, reliance on specific communication schemes, extensive measurement bookkeeping, overly conservative estimates, or restrictive measurement model assumptions. To address these issues, this paper proposes a recursive distributed framework in which each robot only maintains the latest estimate of its own pose and the tracked targets' poses, eliminating the need for storing historical measurements and cross-correlations. Most importantly, our framework supports generic measurement models and allows flexible customization of update methods for different measurements, thus making full use of all available information. Furthermore, an event-triggered communication scheme is implemented, occurring only between robot pairs that share a relative measurement. Extensive Monte Carlo simulations validate the proposed method, demonstrating state-of-the-art accuracy performance among existing distributed methods.