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

This 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.