Linear system identification is a key problem in many important applications, among which
echo cancelation is a very challenging one. Due to the long length impulse responses (ie …

The recursive least-squares (RLS) adaptive filter is an appealing choice in many system
identification problems. The main reason behind its popularity is its fast convergence rate …

This work develops robust diffusion recursive least-squares algorithms to mitigate the
performance degradation often experienced in networks of agents in the presence of …

The identification of room acoustic impulse responses represents a challenging problem in
the framework of many important applications related to the acoustic environment, like echo …

This paper proposes a unified sparsity-aware robust recursive least-squares RLS (S-RRLS)
algorithm for the identification of sparse systems under impulsive noise. The proposed …

There are different strategies to improve the overall performance of the recursive least-
squares (RLS) adaptive filter. In this letter, we focus on the data-reuse approach, aiming to …

Multi-input multi-output (MIMO) detection based on turbo principle has been shown to
provide a great enhancement in the throughput and reliability of underwater acoustic (UWA) …

Due to its fast convergence rate, the recursive least-squares (RLS) algorithm is very popular
in many applications of adaptive filtering, including system identification scenarios …

The l 1-norm sparsity constraint is a widely used technique for constructing sparse models.
In this paper, two zeroattracting recursive least squares algorithms, which are referred to as …

Sparse recovery techniques find applications in many areas. Real-time implementation of
such techniques has been recently an important area for research. In this paper, we propose …