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To improve the performance of the recently presented individual weighting factors sign subband adaptive filter (IWF-SSAF) algorithm, its 2 combination algorithms using different step sizes are proposed. The first algorithm is to convexly combine the weight vectors of a large step-size IWF-SSAF filter and a small step-size one; and the second algorithm is to obtain a time-varying step size for the IWF-SSAF by combining a large step size and a small one. The minimization of the sum of the l1-norm of subband errors is used to indirectly update the mixing parameters in these 2 algorithms through a modified sigmoidal function. Moreover, in the first algorithm, to implement a smooth transition from the large step-size IWF-SSAF filter to the small step-size one, the component filters receive a cyclic feedback of the combined weight vector. Both proposed algorithms have almost the same convergence performance, but the second algorithm saves computational cost. Simulation results in impulsive noise scenarios demonstrate the superiority of our proposed algorithms. [ABSTRACT FROM AUTHOR]

An incremental least‐mean M‐estimate algorithm is proposed for distributed network, which minimises a robust M‐estimator‐based cost function against impulsive noise via the gradient descent method. Moreover, based on the same frame for implementation, an incremental distributed least‐mean p‐power algorithm is presented for combating impulsive noise. Simulations verify the superiority of the proposed algorithms in the presence of impulsive noise relative to some existing incremental distributed algorithms. [ABSTRACT FROM AUTHOR]