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

TL;DR

This paper introduces a sparsified, event-triggered diffusion framework for multi-agent collaborative perception that enhances tracking accuracy and communication efficiency simultaneously.

Contribution

It proposes a novel ET diffusion-based filter combining error minimization and correlation-aware strategies for improved real-time multi-agent target tracking.

Findings

Achieves better tracking accuracy with less data transmission.

Reduces convergence time and communication load.

Demonstrates scalability and effectiveness in experiments.

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.