Linearly-constrained recursive total least-squares algorithm
Date
2012
Authors
Arablouei, R.
Dogancay, K.
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Journal article
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IEEE Signal Processing Letters, 2012; 19(12):821-824
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Abstract
We develop a new linearly-constrained recursive total least squares adaptive filtering algorithm by incorporating the linear constraints into the underlying total least squares problem using an approach similar to the method of weighting and searching for the solution (filter weights) along the input vector. The proposed algorithm outperforms the previously proposed constrained recursive least square (CRLS) algorithm when both input and output data are observed with noise. It also has a significantly smaller computational complexity than CRLS. Simulations demonstrate the efficacy of the proposed algorithm.
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Copyright 2012 IEEE