Abstract: The fixed tap-length distributed adaptive filtering algorithm can achieve the corresponding estimation accuracy only when the tap-length of the unknown vector is assumed to be known as a prior and constant.The convergence performance of the algorithm deteriorates when the tap-length is unknown or time varying.Variable tap-length distributed adaptive filtering algorithm is an effective way to solve this problem.However,most of the distributed variable tap-length adaptive filtering algorithms use the minimum mean square error (MSE) criterion as the cost function of the tap-length,and the convergence of the algorithm is greatly affected under the impulsive noise environment.The maximum correntropy criterion is robust to impulse noise and has low computational complexity.In order to improve the estimation accuracy of the distributed variable tap-length adaptive filtering algorithm under the impulsive noise environment,the maximum correntropy criterion is used as the cost function,relevant results are substituted into the fixed tap-length diffusion maximum correntropy criterion algorithm,and thus a diffusion variable tap-length maximum correntropy criterion (DVTMCC) algorithm is proposed.By communicating with the nodes in the neighborhood,the proposed algorithm realizes the information fusion of the entire network by means of diffusion,which has advantages of a high estimation accuracy,a small calculation cost,etc.Simulation experiments compare the convergence performance of DVTMCC algorithm and other distributed variable tap-length adaptive filtering algorithms,and fix tap-length diffusion maximum correntropy criterion algorithm under the impulsive noise environment.Simulation results show that the DVTMCC algorithm can estimate the tap-length and weight vector of the unknown vector at the same time under the impulsive noise environment,and its performance is better than compared algorithms.

Key words: Adaptive networks, Diffusion strategy, Impulsive noises, Maximum correntropy criterion, Variable tap-length