A mixed probabilistic/bounded-error approach to parameter estimation in the presence of amplitude bounded white noise

In this paper, we propose a mixed approach to identification of linear dynamic systems corrupted by additive, amplitude bounded white noise. To come up with an estimate of the system, we mix the information from a prediction error estimate and a set membership estimate. This approach robustifies the parameter estimates, so that they benefit from both the prediction error and the set membership features. The proposed estimation technique shows good performance on a number of simulation examples, which are performed using a wide range of noise sources.