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- Title
l<sub>2</sub>-norm feature least mean square algorithm l<sub>2</sub>-norm feature least mean square algorithm.
- Authors
Haddad, D. B.; dos Santos, L. O.; Almeida, L. F.; Santos, G. A. S.; Petraglia, M. R.
- Abstract
In many practical applications, systems and signals show energy concentration in a few coefficients. This prior knowledge can often be incorporated into algorithms designed for tasks such as compressive sensing and system identification. This Letter proposes a new least mean square (LMS)-based algorithm that exploits the hidden sparsity of the system that the adaptive filter intends to estimate. The algorithm minimises the l2-norm of a linear transformation of the coefficient vector, using the minimum distortion principle. Simulation results demonstrate good performance of the proposed algorithm with respect to the LMS algorithm. In addition, a stochastic model of the advanced algorithm is proposed, which provides accurate meansquare deviation and mean-square error predictions.
- Subjects
LEAST squares; MEAN square algorithms; ADAPTIVE filters; SYSTEM identification; FORECASTING
- Publication
Electronics Letters (Wiley-Blackwell), 2020, Vol 56, Issue 10, p516
- ISSN
0013-5194
- Publication type
Article
- DOI
10.1049/el.2019.3939