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Minimum variance estimation of parameters constrained by bounds

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2 Author(s)
Monin, A. ; Lab. d''Anal. et d''Archit., CNRS, Toulouse, France ; Salut, G.

This correspondence deals with an extension of minimum variance estimation when the parameter to be estimated is constrained by bounds. It is shown that a particular initial distribution allows finite-dimensional calculation and leads to a nonlinear filter. More precisely, it is shown that a truncated Gaussian distribution is preserved a long time, leading to a finite number of parameters to be computed. Proof of the main theorem is straightforward with significant application such as positive real amplitude estimation. Performance gains are shown on the LORAN-C signal reception example

Published in:

Signal Processing, IEEE Transactions on  (Volume:49 ,  Issue: 1 )

Date of Publication:

Jan 2001

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