An advanced evolutionary algorithm for parameter estimation of the discrete Kalman filter Original Research Article

In this work we design an advanced Evolutionary Algorithm (EA) for optimizing the discrete Kalman filter (KF) model. The EA employs parallel architecture and an advanced mutation operator called the “Selection Follower”. Its performance is benchmarked with that of the Expectation-Maximization algorithm (EM) in minimizing the mean-square-error of the KF prediction. Experimental results show that the EA consistently outperforms the EM and runs significantly faster under the same number of function evaluations.