Diffusion Models for Multi-target Adversarial Tracking
This work addresses the challenge of accurate autonomous target estimation for unmanned vehicles in security applications, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of multi-target adversarial tracking in scenarios like drug-trafficking interdiction by proposing CADENCE, a diffusion model that generates predictions of adversary locations from sparse past data, and it reports that the single-target model outperforms all baselines on Average Displacement Error across all time horizons.
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.