考虑延迟与不确定性，用多智能体强化学习提升反无人机目标定位精度
Delay-Aware Active Triangulation with Uncertainty-Driven Multi-Agent Reinforcement Learning for Counter-UAS
- 基于年龄信息增强的决策框架，让系统感知并补偿通信延迟
- 实测定位误差仅0.547米，三角测量有效率提升至78.1%
- 适合需要高鲁棒性的无人机反制场景，尤其对延迟敏感的任务
多智能体主动视觉三角测量通过协调可控制摄像头的移动观测器，实现对空域目标的精准三维定位。然而，现有方法假设状态反馈瞬时到达，忽略了检测、通信和决策传播带来的累积延迟。本文提出一种面向反无人机应用的延迟感知、不确定性驱动的多智能体强化学习框架。贡献包括：（1）采用带时效性信息（AoI）增强的Dec-POMDP建模，使系统具备延迟感知能力，将三角测量有效性提升10.6个百分点；（2）对比实验表明，感知一致奖励优于理想状态奖励（RMSE：0.547 m vs. 0.633 m，轨迹丢失减少27%），二者使用相同观测噪声但优化目标不同，揭示稳定性与鲁棒性权衡；（3）融合像素、位姿、云台及内参不确定性的多源协方差传播机制，仅考虑角度噪声会导致RMSE恶化2.8倍。在4096个并行环境中的MAPPO实验取得0.547±0.217 m RMSE与78.1%三角测量有效性，而MLP策略有效性仅为0.7%，证实循环记忆对延迟补偿至关重要。
原文摘要 · Abstract (English)
Multi-agent active visual triangulation enables precise 3D localization of aerial targets by coordinating mobile observers with controllable cameras. However, existing methods assume instantaneous state feedback, ignoring cumulative latency from detection, communication, and decision propagation. We present a delay-aware, uncertainty-driven multi-agent reinforcement learning framework for target localization in Counter-UAS applications. Our contributions are: (1) a Dec-POMDP formulation with Age-of-Information (AoI) augmented observations enabling delay-aware coordination -- AoI improves triangulation validity by 10.6 percentage points; (2) a controlled comparison showing that perception-consistent rewards outperform privileged clean-state rewards (0.547 m vs.0.633 m RMSE, 27% fewer track losses) -- both policies are trained through identical observation noise but differ in what they are optimized for, producing a stability-robustness tradeoff; and (3) multi-source analytical covariance propagation incorporating pixel, pose, gimbal, and intrinsics uncertainties -- restricting to angular noise alone causes 2.8-fold RMSE degradation. Experiments with MAPPO in 4096 parallel environments achieve 0.547 +- 0.217 m RMSE with 78.1% triangulation validity, while MLP policies achieve near-zero validity (0.7%), confirming recurrent memory as essential for delay compensation.
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