Open Accessℓ 2 ‐norm feature least mean square algorithm
Author(s) -
Haddad D.B.,
Santos L.O.,
Almeida L.F.,
Santos G.A.S.,
Petraglia M.R.
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.3939
Subject(s) - least mean squares filter , algorithm , norm (philosophy) , mean squared error , adaptive filter , mathematics , system identification , distortion (music) , filter (signal processing) , computer science , statistics , data mining , computer vision , amplifier , computer network , bandwidth (computing) , political science , law , measure (data warehouse)
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 ℓ 2 ‐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.