vii List of Figures x List of Tables xi I Direct Minimization of the Equation Residuals 1

An improved robust variable step-size least mean square (LMS) algorithm is developed in this paper. Unlike many existing approaches, we adjust the variable step-size using a quotient form of filtered versions of the quadratic error. The filtered estimates of the error are based on exponential windows, applying different decaying factors for the estimations in the numerator and denominator. The ...

For least mean-square (LMS) algorithm applications, it is important to improve the speed of convergence vs the residual error trade-off imposed by the selection of a certain value for the step size. In this paper, we propose to use a mixture approach, adaptively combining two independent LMS filters with large and small step sizes to obtain fast convergence with low misadjustment during station...

AbslractThis paper studies the effect of array calibration errors on the performance of various direction 6nding @F) based signal copy algorithms. Unlike blind copy me€hods, this class of algorithms requires an estimate of the directions of arrival (DOA’s) of the signals in order to compute the copy weight vectors. Under the assumption that the observation time is sufficiently long, the followi...

In this paper, first a brief review is given of a fully pipelined algorithm for recursive least squares (RLS) estimation, based on socalled ‘inverse updating’. Then a specific class of (block) RLS algorithms is considered, which embraces normalized LMS as a special case (with block size equal to one). It is shown that such algorithms may be cast in the ‘inverse-updating RLS’ framework. This all...

In this paper we study the impact of network size on the performance of incremental least mean square (ILMS) adaptive networks. Specifically, we consider two ILMS networks with different number of nodes and compare their performance in two different cases including (i) ideal links and (ii) noisy links. We show that when the links between nodes are ideal, increasing the network size improves the...

Autoregressive (AR) modeling is widely used in signal processing. The coefficients of an AR model can be easily obtained with a least mean square (LMS) prediction error filter. However, it is known that this filter gives a biased solution when the input signal is corrupted by white Gaussian noise. Treichler suggested the -LMS algorithm to remedy this problem and proved that the mean weight vect...

In many identi cation and tracking problems, an accurate estimate of the measurement noise variance is available. A partially adaptive LMS-type algorithm is developed which can exploit this information while maintaining the simplicity and robustness of LMS. This noise constrained LMS (NCLMS) algorithm is a type of variable step-size LMS algorithm, which is derived by adding constraints to the m...

A fundamental relationship exists between the quality of an adaptive solution and the amount of data used in obtaining it. Quality is defined here in terms of “misadjustment,” the ratio of the excess mean square error (mse) in an adaptive solution to the min imum possible mse. The higher the misadjustment, the lower the quality is. The quality of the exact least squares solution is compared wit...