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发挥着关键作用，尤其在毒品贩运拦截中，对对抗性目标位置的了解往往有限。提升自主追踪系统将使无人机、水面及水下航行器能够更有效地协助拦截使用有人驾驶水面、半潜及空中船只的走私者。随着无人机数量的激增，精确的自主目标估计对安全保障更为关键。本文提出基于约束智能体的增强型多智能体追踪扩散模型（CADENCE），该方法旨在利用过去稀疏状态信息生成对手位置的全面预测。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预测评估，采用扩散模型的蒙特卡洛采样来估计每个生成轨迹对应的概率。我们提出一种新型基于交叉注意力机制的扩散模型，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。