ℓ2‐norm feature least mean square algorithm

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 algor...

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Bibliographic Details
Published in:Electronics letters 2020-05, Vol.56 (10), p.516-519
Main Authors: Haddad, D.B., Santos, L.O., Almeida, L.F., Santos, G.A.S., Petraglia, M.R.
Format: Article
Language:English
Subjects:
adaptive filter
adaptive filters
coefficient vector
compressive sensing
distortion
energy concentration
hidden sparsity
l2‐norm feature LMS algorithm
least mean squares methods
linear transformation
LMS‐based algorithm
mean‐square deviation
mean‐square error predictions
minimum distortion principle
signal processing
stochastic model
stochastic processes
system identification
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Summary: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 mean‐square deviation and mean‐square error predictions.
ISSN:1350-911X
1350-911X
DOI:10.1049/el.2019.3939