IMPROVEMENT OF OBSERVER KALMAN FILTER IDENTIFICATION (OKID) BY RESIDUAL WHITENING

This paper presents a time-domain method to identify a state space mod el of a linear system and ifs corresponding observer/Kalman filter fro m a given set of general input-output data. The identified filter has the properties that its residual is minimized in the least squares sen se, orthogonal to the time-shifted versions of itself, and to the give n input-output data sequences. The connection between the state space model and a particular auto-regressive moving average description of a linear system is made in terms of the Kalman filter and a deadbeat ga in matrix. The procedure first identifies the Markov parameters of an observer system, from which a state space model of the system and the filter gain are computed. The developed procedure is shown to improve results obtained by an existing observer/Kalman filter identification method, which is based on an auto-regressive model without the moving average terms. Numerical and experimental results are presented to ill ustrate rite proposed method.