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Data-Reuse Recursive Least-Squares Algorithms
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DOI：10.1109/LSP.2022.3153207.png)
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Abstract
En 中文
There are different strategies to improve the overall performance of the recursive least-squares (RLS) adaptive filter. In this letter, we focus on the data-reuse approach, aiming to improve the convergence rate/tracking of the algorithm by reusing the same set of data (i.e., the input and reference signals) several times. First, we present a computationally efficient data-reuse RLS algorithm, which is the result of a low complexity implementation of the data-reuse process. Moreover, we extend the idea to the fast RLS algorithm. Simulations performed in the context of echo cancellation support the performance gain.
Keywords:
Signal processing algorithms
Convergence
Covariance matrices
Computational complexity
Indexes
Kalman filters
Jacobian matrices
Adaptive filters
echo cancellation
data-reuse
recursive least-squares (RLS) algorithm
fast RLS (FRLS) algorithm
convergence
tracking
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W