Identification of Two-Dimensional Causal Systems With Missing Output Data via Expectation–Maximization Algorithm

For 2-D causal systems, the variables depend both on time, and on spatial coordinates. This article develops two identification algorithms for two-dimensional causal systems. First, a maximum likelihood estimation algorithm is developed for two-dimensional causal systems when there is no missing data. Second, an expectation-maximization based auxiliary model algorithm, and an expectation-maximization based modified Kalman filtering and smoothing algorithm are derived for 2-D causal systems with missing outputs. It is demonstrated that the modified Kalman filtering, and smoothing algorithm is more effective for systems with missing outputs. The effectiveness of these two algorithms is verified by a simulation example.