Abstract

The growing relevance of multi-agent systems has drawn increasing focus on communication-efficient filters for collaborative target tracking to alleviate the system's communication burden. While the event-triggered (ET) mechanism can improve communication efficiency in collaborative tracking, 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 tracking, aiming to reduce the system's data transmission without notable 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. Our algorithm's stability has been proved. Results show that our algorithm can reduce estimation error by 21.6-72.18%, decrease computation time by 47.2-62.55%, and improve communication efficiency by 34.42-49.65% compared to mainstream collaborative tracking approaches.