Event-Triggered Diffusion Kalman Filters
This work addresses communication efficiency in distributed sensor networks, offering a practical solution for resource-constrained applications like mobile robotics and wireless systems, though it is incremental as it builds on existing diffusion Kalman filters.
The paper tackles the problem of excessive communication and computation in distributed state estimation for resource-constrained sensor networks by proposing an event-triggered diffusion Kalman filter that reduces message transmissions. Experimental results on a physical testbed show the algorithm saves 86% of communication overhead with only a 16% performance deterioration compared to the original method.
Distributed state estimation strongly depends on collaborative signal processing, which often requires excessive communication and computation to be executed on resource-constrained sensor nodes. To address this problem, we propose an event-triggered diffusion Kalman filter, which collects measurements and exchanges messages between nodes based on a local signal indicating the estimation error. On this basis, we develop an energy-aware state estimation algorithm that regulates the resource consumption in wireless networks and ensures the effectiveness of every consumed resource. The proposed algorithm does not require the nodes to share its local covariance matrices, and thereby allows considerably reducing the number of transmission messages. To confirm its efficiency, we apply the proposed algorithm to the distributed simultaneous localization and time synchronization problem and evaluate it on a physical testbed of a mobile quadrotor node and stationary custom ultra-wideband wireless devices. The obtained experimental results indicate that the proposed algorithm allows saving 86% of the communication overhead associated with the original diffusion Kalman filter while causing deterioration of performance by 16% only. We make the Matlab code and the real testing data available online.