Adaptive Deconvolution Based on Spectral Decomposition

Anders Ahlén and Mikael Sternad

SPIE's 1991 International Symposium on Optical Applied Science and Engineering,
San Diego, CA, vol 1565, pp 130-142, July 21-26, 1991. © 1991 SPIE.

In Pdf (1900K).

Outline:

The paper studies the problem of estimating the input signal to a scalar discrete-time linear system The system is known, while the noise and input spectra are unknown. (This problem differs from that of blind deconvolution, where the system is unknown.)

Abstract:

An adaptive algorithm for estimating the input to a linear system is presented. This explicit self-tuning filter is based on the identification of an innovations model. From that model, input and measurement noise ARMA-descriptions are decomposed, using second order moments.

Identifiability results guarantee a unique decomposition. Main tools in the algorithm are the solution of two linear systems of equations. The filter design is based on the polynomial approach to Wiener filtering.

Related publications:

Paper in IEEE Trans. ASSP 1989 on the design of linear scalar deconvolution estimators.
Earlier Conference paper in IFAC ACASP 1989 on adaptive deconvolution.
Paper in Automatica 1990, where the identifiability conditions are derived.