Non-stationary sparse system identification over adaptive sensor networks with cyclic cooperation

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. 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 and proved that the performance of incremental least means square/forth (ILMS/F) algorithm surpasses all other algorithms as non-stationarity grows. We hope that this work will inspire researchers to look for other advanced algorithms against systems that are both non-stationary and sparse.

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