Expectation maximization identification algorithm for time-delay two-dimensional systems

In time-delay two-dimensional (2-D) systems, the variables not only depend on time but also on spatial coordinates, moreover, more than one input data are subjected to time-delays at each sampling time. In order to overcome these difficulties, this paper develops an expectation maximization (EM) identification algorithm for estimating the 2-D systems. Compared with the traditional compressed sensing recovery algorithm and the redundant rule based estimation algorithm, the EM algorithm in this paper has three integrated key functions, (1) to estimate the parameters and the unknown time-delays simultaneously, (2) to keep the number of the unknown parameters unchanged, (3) to identify the 2-D systems with varying time-delays. The simulations made further guarantees the usefulness of the proposed algorithm.