Robust Multi-Agent Target Tracking in Intermittent Communication Environments via Analytical Belief Merging

2026-04-08

AI Summary

This work addresses the challenge of robust target tracking in GPS-denied and communication-constrained environments, where multi-agent systems struggle to efficiently fuse local beliefs. The authors propose a novel distributed belief fusion method based on optimizing both forward and reverse Kullback–Leibler (KL) divergences, for which they derive the first closed-form analytical solution—eliminating quantization errors inherent in numerical optimization. Additionally, they introduce a spatially aware weighting mechanism that leverages agents’ physical visitation histories to dynamically integrate beliefs across the network. Extensive simulations, comprising tens of thousands of trials under challenging conditions such as sparse communication and severely degraded sensors, demonstrate that the proposed approach significantly suppresses noise and outperforms existing analytical fusion methods in tracking accuracy and robustness.

Abstract

Autonomous multi-agent target tracking in GPS-denied and communication-restricted environments (e.g., underwater exploration, subterranean search and rescue, and adversarial domains) forces agents to operate independently and only exchange information during brief reconnection windows. Because transmitting complete observation and trajectory histories is bandwidth-exhaustive, exchanging probabilistic belief maps serves as a highly efficient proxy that preserves the topology of agent knowledge. While minimizing divergence metrics to merge these decentralized beliefs is conceptually sound, traditional approaches often rely on numerical solvers that introduce critical quantization errors and artificial noise floors. In this paper, we formulate the decentralized belief merging problem as Forward and Reverse Kullback-Leibler (KL) divergence optimizations and derive their exact closed-form analytical solutions. By deploying these derivations, we mathematically eliminate optimization artifacts, achieving perfect mathematical fidelity while reducing the computational complexity of the belief merge to $\mathcal{O}(N|S|)$ scalar operations. Furthermore, we propose a novel spatially-aware visit-weighted KL merging strategy that dynamically weighs agent beliefs based on their physical visitation history. Validated across tens of thousands of distributed simulations, extensive sensitivity analysis demonstrates that our proposed method significantly suppresses sensor noise and outperforms standard analytical means in environments characterized by highly degraded sensors and prolonged communication intervals.

Problem

• multi-agent target tracking
• intermittent communication
• belief merging
• KL divergence
• GPS-denied environments

Innovation

• Analytical Belief Merging
• Kullback-Leibler Divergence
• Multi-Agent Target Tracking
• Intermittent Communication
• Spatially-Aware Weighting