Reference BookDOI
Space-time Kalman filter
Noel A Cressie,Christopher K. Wikle +1 more
- pp 2045-2049
TLDR
The Kalman filter arose out of R.E. Kalman's interest in applying the concept of state vectors to the Wiener filtering problem, and quickly became an essential component of modern control systems theory and practice.Abstract:
The Kalman filter arose out of R.E. Kalman's interest in applying the concept of state vectors to the Wiener filtering problem. The success of this method was evident in early applications to trajectory estimation and control of spacecraft; it was so successful, in fact, that the Kalman filter quickly became an essential component of modern control systems theory and practice. This initial success led to the propagation of Kalman-filtering ideas to other scientific disciplines, within which the methodology was adapted to suit numerous state–space oriented problems. Kalman filtering (of spatial data) is still an active area of research in the atmospheric and oceanic sciences.read more
Citations
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Journal ArticleDOI
Distributed Kriged Kalman Filter for Spatial Estimation
TL;DR: This paper considers robotic sensor networks performing spatially-distributed estimation tasks and designs a gradient ascent cooperative strategy and analyzes its convergence properties in the absence of measurement errors via stochastic Lyapunov functions.
Journal ArticleDOI
Spatiotemporal Learning via Infinite-Dimensional Bayesian Filtering and Smoothing: A Look at Gaussian Process Regression Through Kalman Filtering
TL;DR: Methods for converting spatiotemporal Gaussian process regression problems into infinite-dimensional state-space models are presented and the use of machine-learning models in signal processing becomes computationally feasible, and it opens the possibility to combine machine- learning techniques with signal processing methods.
Journal ArticleDOI
Fixed Rank Filtering for Spatio-Temporal Data
TL;DR: In this article, the authors demonstrate how a Spatio-Temporal Random Effects (STRE) component of a statistical model reduces the problem to one of fixed dimension with a very fast statistical solution, a methodology called Fixed Rank Filtering (FRF).
Journal ArticleDOI
Spatio-temporal smoothing and EM estimation for massive remote-sensing data sets
Matthias Katzfuss,Noel A Cressie +1 more
TL;DR: In this paper, the authors proposed a dimension-reduced mixed-effects model for estimating the parameters and substituting them into the optimal predictors using an empirical-Bayes approach.
Journal ArticleDOI
Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER
Simo Särkkä,Arno Solin,Aapo Nummenmaa,Aapo Nummenmaa,Aki Vehtari,Toni Auranen,Simo Vanni,Fa-Hsuan Lin,Fa-Hsuan Lin,Fa-Hsuan Lin +9 more
TL;DR: The DRIFTER algorithm is introduced, which is a new model based Bayesian method for retrospective elimination of physiological noise from functional magnetic resonance imaging (fMRI) data and it is shown that the method outperforms the RETROICOR algorithm if the shape and amplitude of the physiological signals change over time.
References
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Book ChapterDOI
A New Approach to Linear Filtering and Prediction Problems
TL;DR: In this paper, the clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the?stat-tran-sition? method of analysis of dynamic systems.
Book
Stochastic Processes and Filtering Theory
TL;DR: In this paper, a unified treatment of linear and nonlinear filtering theory for engineers is presented, with sufficient emphasis on applications to enable the reader to use the theory for engineering problems.
Journal ArticleDOI
Statistics for Spatial Data, Revised Edition.
TL;DR: This chapter discusses how to make practical use of spatial statistics in day-to-day analytical work, and some examples from the scientific literature suggest a straightforward and efficient way to do this.