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Recursive multiscale estimation of space-time random fields

TitleRecursive multiscale estimation of space-time random fields
Publication TypeConference Paper
Year of Publication1999
AuthorsHo, T. T., P. Fieguth, and A. S. Wilsky
Conference NameInternational Conference on Image Processing
Keywordscomputational complexity, estimation errors, image processing, large-scale dynamic systems, multiscale models, recursive estimation, recursive multiscale estimation, reduced-order spatially-interpolated multiscale models, space-time adaptive processing, space-time random fields

We recently developed a multiscale recursive estimation procedure for the estimation of large-scale dynamic systems. The procedure propagates multiscale models for the estimation errors more efficiently than the Kalman filter's propagation of the error covariances, with a resulting computational complexity of Oscr;(N) and Oscr;(N3/2 ), where N is the number of variables estimated, for 1-D and 2-D dynamic systems, respectively. To further reduce the computational cost, we introduce in this paper a new class of reduced-order spatially-interpolated multiscale models, and demonstrate their use in remote