Event-Triggered Diffusion Kalman Filters
Abstract – Distributed state estimation depends strongly on collaborative signal processing, which often involves excessive communication and computation on resource-constrained sensor nodes. Therefore, we propose an event-triggered diffusion Kalman filter, which only collects measurements and exchanges messages between nodes based on a local signal indicative of the estimation error. This leads to an energy-aware state estimation algorithm that regulates the resource consumption in wireless networks and ensures the effectiveness of every consumed resource. We apply our algorithm to distributed simultaneous localization and time synchronization. We have evaluated our algorithm on a physical testbed of a mobile quadrotor node and stationary custom ultra-wideband wireless devices. Our experimental results show that we are able to save 86% of the communication overhead, while only introducing 16% performance degradation.