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Recently, the l0-least mean square (l0-LMS) algorithm has been proposed toidentify sparse linear systems by employing a sparsity-promoting continuousfunction as an approximation of l0 pseudonorm penalty. However, the performanceof this algorithm is sensitive to the appropriate choice of the some parameterresponsible for the zero-attracting intensity. The optimum choice for thisparameter depends on the signal-to-noise ratio (SNR) prevailing in the system.Thus, it becomes difficult to fix a suitable value for this parameter,particularly in a situation where SNR fluctuates over time. In this work, wepropose several adaptive combinations of differently parameterized l0-LMS toget an overall satisfactory performance independent of the SNR, and discusssome issues relevant to these combination structures. We also demonstrate anefficient partial update scheme which not only reduces the number ofcomputations per iteration, but also achieves some interesting performance gaincompared with the full update case. Then, we propose a new recursive leastsquares (RLS)-type rule to update the combining parameter more efficiently.Finally, we extend the combination of two filters to a combination of M numberadaptive filters, which manifests further improvement for M > 2.

Adaptive Combination of l0 LMS Adaptive Filters for Sparse System Identification in Fluctuating Noise Power