Abstract
In this paper, to reduce the computational load of the federated Kalman filter, an expectation-maximization federated Kalman filtering (EM-FKF) algorithm for integrated navigation systems is proposed. First, the states with poor estimate accuracies are removed from local filters to reduce the computational load. Then the EM algorithm is applied. More precisely, the common states for each local filter are estimated in E-step of EM algorithm by using a linear Kalman filtering algorithm, and its own unique sensor biases are updated in M-step of EM algorithm. The M-step in the local filters and the fusion of common states in the master filter are performed simultaneously, so the computational burden is further reduced for the federated Kalman filter. The proposed algorithm was evaluated with simulated data first, then an experiment was conducted on a real inertial navigation system, global positioning system, and star sensor (INS/GPS/SS) integrated navigation system to verify the proposed algorithm. The results of the simulation and the experiment demonstrated that the proposed algorithm effectively reduced the computational load, compared with the standard federated Kalman filtering algorithm.
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