Constrain, Correspond, Correct: Distributed Game-Theoretic Data Association for Assignment Games on Multimodal Sensing Grids

Assignment games are a promising framework for autonomous management of a multimodal Tactical Sensing Grid (TSG). They provide theoretical guarantes and exhibit excellent empirical performance in maintaining custody of all observed targets in a scene. However, TSG sub-grid initiation of an assignment game requires multimodal data association between playing nodes (the sub-grid). Playing nodes must achieve consensus on target labels and identities before game play proceeds. Using the locally centralized communication network assumed by an assignment game, we propose the Triple-C distributed method for solving this association problem. The method we propose is suitable for fairly general scenarios, requiring that each node have an intrinsic notion of what constitutes an outlier and what constitutes similarity. At the core of the Triple-C method is a collection of parallelizable assignment games played between two nodes. Triple-C yields theoretical guarantees on output association consistency. We evaluate the performance of the Triple-C method on multiple simulations, showing that it computes associations quickly and accurately, thus enabling a TSG to maintain full situational awareness in a two-vehicle scenario.