Breaking the Communication-Accuracy Trade-off: A Sparsified Information Diffusion Framework for Multi-Agent Collaborative Perception

2026-05-01

AI Summary

This work addresses the challenge of balancing communication efficiency and estimation accuracy in multi-agent cooperative perception by proposing an event-triggered sparsified information diffusion framework (EDC-CIF). The method integrates an error-minimizing event-triggering mechanism with cubature information filtering for local state estimation and employs a correlation-aware diffusion strategy to enable efficient global fusion. Both theoretical analysis and experimental results demonstrate that EDC-CIF overcomes the traditional trade-off between communication overhead and estimation performance, significantly reducing communication volume and computational time while simultaneously improving tracking accuracy and convergence speed. The framework exhibits strong scalability, making it well-suited for large-scale multi-agent systems.

Abstract

The growing relevance of multi-agent systems has drawn increasing focus on communication-efficient filters for collaborative perception to alleviate the system's communication burden. While the event-triggered (ET) mechanism can improve communication efficiency in collaborative state estimation, an inevitable trade-off exists between estimation accuracy and communication cost in ET filters. This paper proposes a fast and accurate ET diffusion-based filter for real-time multi-agent collaborative target tracking, aiming to reduce the system's data transmission without compromise in tracking performance. The proposed filter achieves improved tracking accuracy, reduced data transmission, and accelerated convergence using an error-minimized ET cubature information filter (CIF) for local estimation, and a correlation-aware diffusion strategy for global fusion. The experimental results confirm the scalability of the proposed EDC-CIF algorithm and demonstrate its efficacy in simultaneously reducing estimation error and computation time while significantly enhancing communication efficiency.

Problem

• communication-accuracy trade-off
• multi-agent collaborative perception
• event-triggered filtering
• collaborative state estimation
• communication efficiency

Innovation

• event-triggered filtering
• information diffusion
• collaborative perception
• communication efficiency
• cubature information filter