English(EN) Reinforcement Learning Trained Observer Control for Bearings-Only Tracking

强化学习提升自主目标跟踪的精度和鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-06 04:00

研究人员开发了一种深度强化学习方法，用于自主跟踪运动目标的仅方位跟踪。该系统将观测器机动问题表述为信念马尔可夫决策过程，并使用立方体卡尔曼滤波器来表示信念状态。奖励函数在最小化估计误差和保持滤波器一致性之间取得平衡，并使用深度Q网络在50,000个回合中训练了策略。 AI

影响在仅方位场景中，引入了一种新颖的基于强化学习的控制策略，以提高目标跟踪的精度和鲁棒性。

排序理由这是一篇研究论文，详细介绍了强化学习在特定跟踪问题中的新颖应用。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Branko Ristic, Sanjeev Arulampalam · 2026-05-06 04:00

基于强化学习训练的观测器控制用于仅方位跟踪

arXiv:2605.02120v1 Announce Type: new Abstract: This paper develops a deep reinforcement learning based observer control policy for autonomous bearings-only tracking of a moving target. The observer manoeuvre problem is formulated as a belief Markov decision process, where the be…