Group sparse LMS for multiple system identification

Lei Yu 1 Chen Wei 1 Gang Zheng 2, 3
3 NON-A - Non-Asymptotic estimation for online systems
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : Armed with structures, group sparsity can be exploited to extraordinarily improve the performance of adaptive estimation. In this paper, a group sparse regularized least-mean-square (LMS) algorithm is proposed to cope with the identification problems for multiple/multi-channel systems. In particular, the coefficients of impulse response function for each system are assumed to be sparse. Then, the dependencies between multiple systems are considered, where the coefficients of impulse responses of each system share the same pattern. An iterative online algorithm is proposed via proximal splitting method. At the end, simulations are carried out to verify the superiority of our proposed algorithm to the state-of-the-art algorithms.
Type de document :
Communication dans un congrès
23rd European Signal Processing Conference , Aug 2015, Nice, France. 2015, 〈10.1109/EUSIPCO.2015.7362672〉
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Contributeur : Gang Zheng <>
Soumis le : jeudi 7 janvier 2016 - 16:08:12
Dernière modification le : mercredi 25 avril 2018 - 15:43:08

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Lei Yu, Chen Wei, Gang Zheng. Group sparse LMS for multiple system identification. 23rd European Signal Processing Conference , Aug 2015, Nice, France. 2015, 〈10.1109/EUSIPCO.2015.7362672〉. 〈hal-01252391〉

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