Recursive multiscale estimation of 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 𝒪(N) and 𝒪(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.