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Random process reconstruction from multiple noisy source observations

Lacaze, Bernard and Mailhes, Corinne Random process reconstruction from multiple noisy source observations. (2004) Sampling Theory in Signal and Image Processing, 3 (3). 257-277. ISSN 1530-6429

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Official URL: http://stsip.org/vol03/no3/vol03no3pp257-277.html


The problem addressed in this paper is the reconstruction of a continuous-time stationary random process from noisy sampled observations coming from different sources. An optimal solution in terms of linear filtering of observed samples is derived and the expression of the corresponding minimum reconstruction error power is given. Moreover, two equivalent reconstruction schemes are given. The first one is recursive, involving two filter banks. Its main interest is that adding or suppressing an input does not affect the whole scheme. The second scheme is symmetric and uses only one filter bank. However, to add a new input requires a complete modification of all the filter transfer functions. Simulation examples are given to prove the application of the reconstruction scheme

Item Type:Article
HAL Id:hal-04288685
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Laboratoire de recherche en télécommunications spatiales et aéronautiques - TéSA (FRANCE)
Laboratory name:
Deposited On:05 Nov 2009 09:05

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