Unbiased estimation of parameters of linear continuous model based on bias correction with input/output measurement noises

In this paper, a algorithm, which is about unbiased parameters estimation of the model of linear continuous system with noise, was put forward. Using modulation function, through the digital modulating integral to the continuous-time model, an equivalent discrete identification model which is parameterized with continuous time model parameters is developed. Then based on this, parameters identification was conducted by the new biased correction method proposed in this paper, which introduced a new vector mainly by parameter of the transfer function, simultaneously producing a new regression vector. The special structure of this new parameter vector makes the deviation calculation with noise can eventually result in unbiased parameter estimation through the biased correction method. The simulation with a real example shows this method has certain advantages.