Extended self‐tuning predictors, smoothers and filters for two‐dimensional data fields
G. R. Wagner, Peter E. Wellstead · International Journal of Adaptive Control and Signal Processing · 1990
Abstract The paper describes new self‐tuning algorithms for two‐dimensional signal processing. Specifically, extended algorithms are developed for filtering, smoothing and prediction of two‐dimensional data fields. These algorithms provide a transfer function approach to the problems of smoothing and prediction in two‐dimensional space with arbitrary lag and lead. As such, the fully two‐dimensional self‐tuning smoother and predictor derived in this paper represent an important generalization of the previous work which concerned prediction and filtering with arbitrary lead/lag on the current line. The generalization is in two parts. The first concerns the formal extension of the theory. The second concerns the development of efficient algorithms for calculation of the extended predictors and filters, with special attention being paid to the approximations required in order to realize the algorithms.