Linear System Identification via Backward-Time Observer Models

This paper presents an algorithm to identify a state-space model of a linear system using a backward-time approach. The procedure consists of three basic steps. First, the Markov parameters of a backward-time observer are computed from experimental input-output data. Second, the backward-time observer Markov parameters are decomposed to obtain the hackward-time system Markov parameters (backward-time pulse response samples) from which a backward-time state-space model is realized using the eigensystem realization algorithm. Third, the obtained hackward-time state-space model is converted to the usual forward-time representation. Stochastic properties of this approach will he discussed. Experimental results are given to illustrate when and to what extent this concept works.