Enhancing the tracking capability of recursive least p-norm algorithm via adaptive gain factor

The main drawback of the recursive least p-norm (RLpN) adaptive-filtering algorithm is a poor tracking performance in the presence of abrupt changes in the model. In this paper, a new method to enhance tracking capability of the RLpN (ET-RLpN) algorithm is proposed, which uses the adaptive gain factor in the cross-correlation vector and the input-signal autocorrelation matrix to enhance tracking capability. Simulation results in system identification and echo cancellation applications are presented, which demonstrate that the ET-RLpN achieves improved tracking capability compared to the conventional RLpN and controlled adaptive combination of two RLpN filters (CAC-RLpN).