Distributed Kalman filtering with adaptive communication

This work proposes an adaptive event-triggered communication framework for distributed state estimation in sensor networks, enabling each node to self-adapt its transmission rule while maintaining a desired average rate and complying with an upper bound on individual transmission rates. Unlike existing approaches that require manual tuning, the proposed method dynamically tunes transmission thresholds, achieving uniform energy and bandwidth consumption across nodes. We analyze the theoretical properties of the proposed transmission strategy, and verify its effectiveness through simulations in a target-tracking scenario.