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arXiv · 1703.08284

Diffusion L0-norm constraint improved proportionate LMS algorithm for sparse distributed estimation

Zongsheng Zheng·Zhigang Liu
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Abstract

To exploit the sparsity of the considered system, the diffusion proportionate-type least mean square (PtLMS) algorithms assign different gains to each tap in the convergence stage while the diffusion sparsity-constrained LMS (ScLMS) algorithms pull the components towards zeros in the steady-state stage. In this paper, by minimizing a differentiable cost function that utilizes the Riemannian distance between the updated and previous weight vectors as well as the L0 norm of the weighted updated weight vector, we propose a diffusion L0-norm constraint improved proportionate LMS (L0-IPLMS) algorithm, which combines the benefits of the diffusion PtLMS and diffusion ScLMS algorithms and performs the best performance among them. Simulations in a system identification context confirm the improvement of the proposed algorithm.

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Zongsheng Zheng, Zhigang Liu. 2017-03-24. Diffusion L0-norm constraint improved proportionate LMS algorithm for sparse distributed estimation. https://arxiv.org/abs/1703.08284

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