A New Kalman Filter Based 2-D AR Model Parameter Estimation Method

This paper presents a new method for the causal quarter-plane (QP) region of support two dimensional (2-D) autoregressive model parameters estimation based on the Kalman filter (KF). To achieve this aim, the corresponding relations are extended to the 2-D case and the related algorithm is presented. Online parameter estimation, capability of parameters variation detection, estimation improvement by using new data and less computational requirement are the significant advantages of the proposed method. As a result of not involving complex and time consuming matrix computations, the presented method is computationally efficient. Numerical simulation is presented to show the efficiency of the proposed approach.