Robust Multi-Agent Target Tracking in Intermittent Communication Environments via Analytical Belief Merging
2026-04-08 • Robotics
Robotics
AI summaryⓘ
The authors studied how teams of robots can track targets when they can't use GPS and have limited ways to talk to each other. Instead of sharing all their detailed info, the robots share simplified maps that capture what they know about the target's location. The authors found exact math formulas to combine these maps without the usual errors caused by computer approximations. They also created a new method that gives more weight to information from places the robots visited more often. Tests showed their method is better at handling sensor mistakes and long communication gaps than older approaches.
multi-agent systemstarget trackingGPS-denied environmentsbelief mergingKullback-Leibler divergenceprobabilistic belief mapsdecentralized estimationsensor noisecommunication constraintsanalytical solution
Authors
Mohamed Abdelnaby, Samuel Honor, Kevin Leahy
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.