The multiresolution character of collocation |
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| Authors: | C Kotsakis |
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| Institution: | (1) Department of Geomatics Engineering, University of Calgary, 2500 University Drive N.W., Calgary, Alberta, Canada T2N 1N4 e-mail: ckotsaki@ucalgary.ca; Tel.: +1-403-2204113; Fax: +1-403-2841980, CA |
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| Abstract: | An interesting theoretical connection between the statistical (non-stochastic) collocation principle and the multiresolution/wavelet
framework of signal approximation is presented. The rapid developments in multiresolution analysis theory over the past few
years have provided very useful (theoretical and practical) tools for approximation and spectral studies of irregularly varying
signals, thus opening new possibilities for `non-stationary' gravity field modeling. It is demonstrated that the classic multiresolution
formalism according to Mallat's pioneering work lies at the very core of some of the general approximation principles traditionally
used in physical geodesy problems. In particular, it is shown that the use of a spatio-statistical (non-probabilistic) minimum
mean-square-error criterion for optimal linear estimation of deterministic signals, in conjunction with regularly gridded
data, always gives rise to a generalized multiresolution analysis in the Hilbert space L
2(R), under some mild constraints on the spatial covariance function and the power spectrum of the unknown field under consideration.
Using the theory and the actual approximation algorithms associated with statistical collocation, a new constructive framework
for building generalized multiresolution analyses in L
2(R) is presented, without the need for the usual dyadic restriction that exists in classic wavelet theory. The multiresolution and `non-stationary' aspects of the statistical collocation approximation
procedure are also discussed, and finally some conclusions and recommendations for future work are given.
Received: 26 January 1999 / Accepted: 16 August 1999 |
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| Keywords: | : Multiresolution approximation – Spatio-statistical approximation – Wavelets – Collocation |
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