Investigations on Sparse System Identification with l0-LMS, Zero-Attracting LMS and Linearized Bregman Iterations

Description

In many system identification applications, the impulse response vector is of sparse nature. Dedicated sparse identification algorithms like the l0-LMS, Zero-Attracting LMS and Linearized Bregman Iterations outperform the traditional LMS algorithm. This imporved performance is achieved by extending the cost function with a L0 or L1 part of the estimated impulse response vector. In this presentation the three algorithms are compared regarding mean-square error of the estimated impulse response, convergence speed, and resulting sparsity.

Period	22 Feb 2017
Event title	International Conference on Computer Aided Systems Theory (EUROCAST 2017)
Event type	Conference
Location	SpainShow on map