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

2026-05-05 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

Researchers from Tsinghua University and Shenzhen Smart City Technology Development Group have developed the Event-Triggered Diffusion-based Cubature Information Filter with Covariance Intersection (EDC-CIF), a novel framework for multi-agent collaborative target tracking. This filter addresses the inherent trade-off between estimation accuracy and communication efficiency in multi-agent systems by integrating an error-minimized event-triggered cubature information filter (ET-CIF) for local estimation and a correlation-aware diffusion strategy for global fusion. The EDC-CIF algorithm aims to reduce data transmission and accelerate convergence without compromising tracking performance. Experimental results, including both numerical simulations and real-world multi-UAV tracking scenarios with LiDAR and RGB-D cameras, confirm its scalability and efficacy in simultaneously reducing estimation error and computation time while significantly enhancing communication efficiency compared to existing methods like C-CKF, EC-EKF/UKF/CKF, D-CIF, and DC-CIF.

Key takeaway

For research scientists developing multi-agent collaborative perception systems, you should consider implementing the EDC-CIF framework. Its design effectively mitigates the communication-accuracy trade-off, offering superior tracking accuracy, faster convergence, and reduced data transmission. This approach is particularly beneficial for real-time applications like multi-UAV target tracking where network resources are constrained and high precision is critical, enabling more robust and efficient system deployments.

Key insights

EDC-CIF balances multi-agent tracking accuracy, response speed, and communication efficiency by optimizing local and global filtering.

Principles

• Minimize error covariance for ET filters.
• Diffusion fusion converges faster than consensus.
• Covariance Intersection handles unknown sensor correlations.

Method

EDC-CIF uses an error-minimized ET-CIF for local estimation and a correlation-aware diffusion fusion strategy with Covariance Intersection for global fusion, transmitting information contributions and correlation matrices.

In practice

• Apply ET-CIF for communication-efficient local state estimation.
• Utilize diffusion fusion for faster multi-agent data aggregation.
• Employ Covariance Intersection to fuse data with unknown correlations.

Topics

• Multi-Agent Collaborative Perception
• Event-Triggered Filtering
• Cubature Information Filter
• Diffusion Fusion Strategy
• Covariance Intersection

Code references

• zhajirong/TMC-EDC-CIF

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.