A new approach for identifying noisy input-output FIR models

This paper proposes an efficient algorithm for identifying FIR models when also the input is assumed as affected by additive noise. This procedure is more accurate than instrumental variables approaches and, differently from total least squares, does not require the a priori knowledge of the ratio b...

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Bibliographic Details
Published in:	2008 3rd International Symposium on Communications, Control and Signal Processing pp. 1548 - 1552
Main Authors:	Diversi, R., Guidorzi, R., Soverini, U.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-03-2008
Subjects:	Additive noise Computer science Finite impulse response filter Instruments Least squares methods Signal processing Signal processing algorithms Signal to noise ratio Statistics Testing
Online Access:	Get full text
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Summary:	This paper proposes an efficient algorithm for identifying FIR models when also the input is assumed as affected by additive noise. This procedure is more accurate than instrumental variables approaches and, differently from total least squares, does not require the a priori knowledge of the ratio between the input and output noise variances. The accuracy of the whole procedure has been tested by means of Monte Carlo simulations and compared with that of compensated and total least squares ones.
ISBN:	1424416876 9781424416875
DOI:	10.1109/ISCCSP.2008.4537473