Identification of systems from non-uniformly sampled data

Abstract

The identification of continuous time models from non-uniformly sampled data records is investigated and a new identification algorithm based on the state variable filter approach is derived. It is shown that the orthogonal least squares estimator can be adapted for the identification of continuous time models from non-uniformly sampled data records and instrumental variables are introduced to reduce the bias in stochastic system identification. Multiplying the filtered variables obtained from the state variable filter, with higher powers of the noise free output signal prior to the estimation, is shown to enhance the parameter estimates. Simulated examples are included to illustrate the models.