Continuous-Time Model Identification and State Estimation Using Non-Uniformly Sampled Data

This paper presents theory, algorithms and validation results for system identification of continuous-time state-space models from finite non-uniformly sampled input-output sequences. The algorithms developed are methods of model identification and stochastic realization adapted to the continuous-time model context using non-uniformly sampled input-output data. The resulting model can be decomposed into an input-output model and a stochastic innovations model. For state estimation dynamics, we have designed a procedure to provide separate continuous-time temporal update and error-feedback update based on non-uniformly sampled input-output data. Stochastic convergence analysis is provided.