  Target tracking plays a crucial role in real-world scenarios, particularly in
drug-trafficking interdiction, where the knowledge of an adversarial target's
location is often limited. Improving autonomous tracking systems will enable
unmanned aerial, surface, and underwater vehicles to better assist in
interdicting smugglers that use manned surface, semi-submersible, and aerial
vessels. As unmanned drones proliferate, accurate autonomous target estimation
is even more crucial for security and safety. This paper presents Constrained
Agent-based Diffusion for Enhanced Multi-Agent Tracking (CADENCE), an approach
aimed at generating comprehensive predictions of adversary locations by
leveraging past sparse state information. To assess the effectiveness of this
approach, we evaluate predictions on single-target and multi-target pursuit
environments, employing Monte-Carlo sampling of the diffusion model to estimate
the probability associated with each generated trajectory. We propose a novel
cross-attention based diffusion model that utilizes constraint-based sampling
to generate multimodal track hypotheses. Our single-target model surpasses the
performance of all baseline methods on Average Displacement Error (ADE) for
predictions across all time horizons.
