On how the distributed Kalman filter is related to the federated Kalman filter

In this paper, a direct connection between the covariance debiasing methodology for the distributed Kalman (DKF) filter in [1] and the federated Kalman filter is shown. In particular, it can be seen that for a unique choice of the information gain hypothesis of the DKF, the covariance debiasing becomes equivalent to the federated Kalman filter. As the complexity of the covariance calculation for the federated Kalman filter is rather low, a hybrid solution is proposed. A numerical evaluation presents two different scenarios where the state estimate of the distributed Kalman filter outperforms the federated Kalman filter in terms of accuracy. The first scenario is using linear Gaussian noise on position measurements whereas in the second scenario a distributed radar application is shown.

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