VARIABLE STEP SIZE KALMAN FILTER USING EVENT HANDLING ALGORITHM FOR SWITCHING SYSTEMS

This paper presents a novel variable step size Kalman Filter by augmenting the event handling procedure of Ordinary Differential Equation (ODE) solvers with the predictor-corrector scheme of well-known discrete Kalman Filter (KF). The main goal is to increase the estimation performance of Kalman Filter in the case of switching/stiff systems. Unlike fixed step size Kalman Filter the sample time (ST) is adapted in the proposed approach based on current estimation performance (KF innovation) of system states and can change during the estimation procedure. The proposed event handling algorithm consists of two main parts: relaxing ST and restricting ST. Relaxing procedure is used to avoid high computational time when no rapid change exists in system dynamics. Restricting procedure is considered to improve the estimation performance by decreasing the Kalman filter step size in the case of fast dynamical behavior (switching behavior). The accuracy and computational time are controlled by using design parameters. The effectiveness of the proposed approach is verified by simulation results using the bouncing ball example as a switching system.