Robust Distributed Kalman filtering with Event-Triggered Communication

TL;DR

This paper introduces two robust distributed Kalman filtering methods for sensor networks that operate under data transmission constraints and model uncertainty, ensuring stability and bounded estimation errors.

Contribution

It proposes novel event-triggered distributed filtering strategies that account for model uncertainty and guarantee stability in sensor networks.

Findings

Both methods are stable with bounded mean-square estimation error.

The strategies effectively handle data transmission constraints.

The filters are robust to model uncertainties within a specified ball.

Abstract

We consider the problem of distributed Kalman filtering for sensor networks in the case there are constraints in data transmission and there is model uncertainty. More precisely, we propose two distributed filtering strategies with event-triggered communication where the state estimators are computed according to the least favorable model. The latter belongs to a ball about the nominal model. We also show that both the methods are stable in the sense that the mean-square of the state estimation error is bounded in all the nodes.