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.

翻译：目标追踪在现实场景中扮演着关键角色，尤其是在毒品贩运拦截中，通常难以获知对抗性目标的位置。改进自主追踪系统将使无人飞行器、水面舰艇和水下航行器能够更好地协助拦截使用有人驾驶水面、半潜及飞行船只的走私者。随着无人机数量的激增，精确的自主目标估计对于安全保障变得更为重要。本文提出了一种名为“面向增强型多智能体追踪的约束智能体扩散方法”（CADENCE）的技术，该方法旨在通过利用先前的稀疏状态信息，生成对抗性位置的综合预测。为评估该方法的有效性，我们在单目标和多目标追踪环境中进行了预测评估，采用扩散模型的蒙特卡洛采样来估计每条生成轨迹对应的概率。我们提出了一种基于交叉注意力的新型扩散模型，该模型利用约束采样生成多模态轨迹假设。在平均位移误差（ADE）指标上，我们的单目标模型在所有时间跨度的预测中均超越了所有基线方法。