Event‐triggered consensus Kalman filtering for time‐varying networks and intermittent observations

Abstract

AbstractThis work introduces an improved design approach for distributed event‐triggering consensus Kalman filtering (DETCKF). We consider a network of sensors that are able to observe a linear discrete‐time stochastic process with known dynamics. The sensors cooperate through information exchange over a possibly time‐varying communication network to obtain an estimate of the process state. We propose event‐triggering conditions and consensus gains for which the error dynamics of the filter are stable. Both are derived from a Lyapunov‐based stability analysis. We also propose an event‐triggering scheme for scenarios in which some of the agents have intermittent observability of the process—that is, they are not able to measure the process dynamics. Under mild conditions on the sensing architecture, we show that even in this case it is possible to design a stable event‐triggered estimate of the process state. We validate our results with numerical simulations and compare with other solutions in the literature.