Adaptive Deconvolution Based on Spectral Decomposition
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).
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.)
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:
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.