Distributed Kalman filtering with reduced transmission rate

The centralized Kalman filter can be implemented in such a way that the required calculations can be distributed over multiple nodes in a network, each of which processes only the locally acquired sensor data. The main downside of this implementation is that it requires each distributed sensor node to communicate with the fusion center in every time step so as to compute the optimal state estimate. In this paper, two distributed Kalman filtering algorithms are proposed to overcome these limitations. The first algorithm merely requires communication of each local sensor node with the fusion center in every other time step. The second algorithm even allows for a lower communicate rate. Both algorithms apply event-based communication to compute consistent estimates and to reduce the estimation error for a fixed communication rate. Simulations demonstrate that both algorithms perform better in terms of the mean squared estimation error than the centralized Kalman filter.