Non-stationary Sparse System Identification over Adaptive Sensor Networks with Diffusion and Incremental Strategies

in this paper we studied the performance of several distributed adaptive algorithms for non-stationary sparse system identification. Non-stationarity is a feature that is introduced to adaptive networks recently and makes the performance of them degraded. We analyzed the performance of both incremental and diffusion cooperation strategies in this newly presented case. The performance analyses are carried out with the steady-state mean square deviation (MSD) criterion of adaptive algorithms. Some sparsity aware algorithms are considered in this paper which tested in non-stationary systems for the first time. It is presented that for incremental cooperation, the performance of incremental least means square/forth (ILMS/F) algorithm surpasses all other algorithms as non-stationarity grows and for diffusion cooperation, the performance of adapt-then-combine (ATC) diffusion prevails reweighted zero attracting (RZA) ATC diffusion algorithm in non-stationary system identification. We hope that this work will inspire researchers to look for other advanced algorithms against systems that are both non-stationary and sparse.