Identification of dual‐rate sampled errors‐in‐variables systems with time delays

Abstract

AbstractFor identification of dual‐rate sampled errors‐in‐variables (EIV) systems with time delays, this paper utilizes the polynomial transformation technique and the redundant rule to derive the augmented dual‐rate model, and proposes a bias compensation‐based least squares (BC‐LS) algorithm. The basic idea is to obtain a biased parameter estimate by using the LS algorithm, then compensate the noise‐induced bias by the estimated noise variances, and finally estimate the time delay according to the unbiased parameter estimate and the given threshold. Considering that proper threshold selection has great influence on the performance of the BC‐LS identification algorithm for high noise level, the generalized orthogonal matching pursuit (GOMP) algorithm is introduced to simultaneously estimate the biased parameters and the time delay, and the bias compensation‐based GOMP (BC‐GOMP) algorithm is further proposed to identify the dual‐rate sampled EIV systems with time delays. The simulation examples demonstrate the effectiveness of the proposed two algorithms, and the BC‐GOMP algorithm performs better especially when the modeling dataset is small.