title: An online novel adaptive filter for denoising time series measurements.
abstract: A nonstationary form of the Wiener filter based on a principal components analysis is described for filtering time series data possibly derived from noisy instrumentation. The theory of the filter is developed, implementation details are presented and two examples are given. The filter operates online, approximating the maximum a posteriori optimal Bayes reconstruction of a signal with arbitrarily distributed and non stationary statistics.
authors.affiliation: Sputnik Consulting, c/o Mail Drop 2160 Syncrude, Mildred Lake Plant Site, P Bag 4009, Fort McMurray AB, 79H 3L1 Canada.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Willis
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D009862:Online Systems; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D013997:Time Factors