Evolutionary programming Kalman filter

A robust Kalman filtering (KF) algorithm based on the evolutionary programming (EP) technique is proposed in this paper, for uncertain systems with unknown-but-bounded uncertain parameters which are described by interval systems. This algorithm takes advantage of the global optima-searching capability of EP to find the optimal KF results at every iteration, which include both the upper–lower boundaries and the nominal trajectory of the optimal estimates of the system state vectors. One prominent feature of this EP filtering algorithm is that it assumes the same statistical conditions and provides the same optimal estimates as the conventional KF scheme. Both linear and nonlinear systems are studied. Two typical computer simulation examples are given with comparison, which verify the merits of the new method – it yields more accurate estimation results and is less conservative as compared to the existing interval Kalman filtering (IKF).