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access icon free Improved -RLS adaptive filter

© The Institution of Engineering and Technology
Received 11/09/2017, Published 07/11/2017

In this Letter, the authors present an improved sparse recursive least squares (RLS) algorithm, which employs a novel approximation of the norm of the filter coefficient vector for regularising the RLS cost function. The proposed algorithm achieves improved performance over existing algorithms as demonstrated via numerical simulations.

Inspec keywords: least squares approximations; adaptive filters; vectors

Other keywords: RLS cost function; improved l0-RLS adaptive filter; filter coefficient vector; numerical simulations; l0 norm approximation; sparse recursive least squares algorithm

Subjects: Passive filters and other passive networks; Interpolation and function approximation (numerical analysis)

References

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      • 4. Horn, R., Johnson, C.R.: ‘Matrix analysis’ (Cambridge University, Cambridge, 1985).
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      • 3. Hong, X., Gao, J., Chen, S.: ‘Zero attracting recursive least squares algorithms’, Trans. Veh. Technol., 2017, 66, (1), pp. 213–221.
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http://iet.metastore.ingenta.com/content/journals/10.1049/el.2017.3441
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