On the Parameter Estimation of Pulse Transfer Function In the Presence of Input and Output Noise

Abstract

Many identification methods are based on the assumption that input measurement is noise-free. However, this condition is not satisfied in most practical situations. In the presence of input noise, those methods have been shown to give erroneous results. Bias-compensated least-squares method is a consistent estimation method for unknwon parameters of pulse transfer function in the presence of input and output noise. The method is based on compensation of asymptotic bias on the least-squares estimates by making use of noise variances estimates. In this paper, a new type of noise variances estimation method is proposed to overcome drawbacks of previously proposed methods. The main feature of proposed method is to introduce a generalized least-squares type estimator in order to estimate noise variances. The results of a simulated example indicate that the proposed method provides good estimates.