Recursive least-squares (RLS) algorithms are widely used in many applications, such as real-time signal processing, control and communications. In some regularization of the provides robustness enhances performance. Interestingly, updating parameter processing data continuously time is a desirable strategy to improve performance applications beamforming. While presented works literature assume ...

Recursive least-squares adaptive decision-feedback equalizers (DFE) can be classified into the direct adaptation (DA) and channel-estimation-based adaptation (CEBA) approaches. CEBA provides better performance than DA when the channel length is known exactly. However, for wireless channels, the channel length is a random unknown quantity. In this paper, we evaluate the performance of the DA and...

The recursive least squares (RLS) algorithm is well known and has been widely used for many years. Most analyses of RLS have assumed statistical properties of the data or the noise process, but recent robust H∞ analyses have been used to bound the ratio of the performance of the algorithm to the total noise. In this paper, we provide an additive analysis bounding the difference between performa...

A new algorithm, multiple concurrent recursive least squares (MCRLS) is developed for parameter estimation in a system having a set of governing equations describing its behavior that cannot be manipulated into a form allowing (direct) linear regression of the unknown parameters. In this algorithm, the single nonlinear problem is segmented into two or more separate linear problems, thereby enab...

Recursive estimates of large systems of equations in the context of least squares fitting is a common practice in different fields of study. For example, recursive adaptive filtering is extensively used in signal processing and control applications. The necessity of solving least squares problem via recursive algorithms comes from the need of fast real-time signal processing strategies. Computa...