Delay-Aware Active Triangulation with Uncertainty-Driven Multi-Agent Reinforcement Learning for Counter-UAS

2026-07-07

AI Summary

This work addresses the degradation in 3D localization accuracy in anti-drone systems caused by existing methods’ neglect of cumulative delays in detection, communication, and decision propagation within multi-agent active visual triangulation. To mitigate this, the authors propose a delay-aware, uncertainty-driven multi-agent reinforcement learning framework that enhances observation modeling in decentralized partially observable Markov decision processes (Dec-POMDPs) using Age of Information (AoI), introduces a perception-consistent reward mechanism, and—uniquely—integrates multi-source uncertainties from pixels, poses, gimbals, and intrinsic camera parameters into covariance propagation. Experiments in 4,096 parallel environments demonstrate a root-mean-square error of 0.547 ± 0.217 meters and 78.1% triangulation validity, outperforming angle-noise-only models by a 2.8× reduction in error and vastly exceeding MLP-based policies (validity <0.7%), thereby confirming the critical role of recurrent memory in compensating for system delays.

Abstract

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.

Problem

• delay-aware
• active triangulation
• multi-agent reinforcement learning
• Counter-UAS
• state feedback latency

Innovation

• delay-aware reinforcement learning
• active triangulation
• Age-of-Information (AoI)
• uncertainty propagation
• multi-agent coordination