Dynamically Regularized Fast Recursive Least Squares
نویسنده
چکیده
This paper introduces a dynamically regularized fast recursive least squares (DR-FRLS) adaptive filtering algorithm. Numerically stabilized FRLS algorithms exhibit reliable and fast convergence with low complexity even when the excitation signal is highly self-correlated. FRLS still suffers from instability, however, when the condition number of the implicit excitation sample covariance matrix is very high. DR-FLRS, overcomes this problem with a regularization process which only increases the computational complexity by 50%. The benefits of regularization include: 1) the ability to use small forgetting factors resulting in improved tracking ability and 2) better convergence over the standard regularization technique of noise injection. Also, DR-FLRS allows the degree of regularization to be modified quickly without restarting the algorithm.
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