A family of adaptive filtering algorithms for processing signals which have energy concentrated in a relatively small number of component subspaces in the spectral domain is introduced. The approach is based on transform domain signal decomposition and linear least squares filtering of the selected subset of transform domain signal components. The derivation is based on the linear least squares...

In this paper, we deal with deterministic dominance of stochastic equations. The obtained results lead to the evaluation of tight upper-bounds upon the parameter tracking error of the forgetting factor recursive least squares (RLS) algorithm applied to the identiication of time-varying systems.

This paper develops low-complexity adaptive receivers for space-time block-coded (STBC) transmissions over frequency-selective fading channels. The receivers are useful for equalization purposes for single user transmissions and for joint equalization and interference cancellation for multiuser transmissions. The receivers exploit the rich code structure of STBC codes in order to deliver recurs...

We consider regularized least-squares (RLS) with a Gaussian kernel. We prove that if we let the Gaussian bandwidth σ → ∞ while letting the regularization parameter λ→ 0, the RLS solution tends to a polynomial whose order is controlled by the rielative rates of decay of 1 σ2 and λ: if λ = σ−(2k+1), then, as σ →∞, the RLS solution tends to the kth order polynomial with minimal empirical error. We...

This paper presents the current progress on a trajectory estimation of a flying object for a robotic catching system, using general purpose and readily available hardware. A recursive least squares (RLS) algorithm is used to extract and predict the position of a flying object in a 3D environment with the information gathered from only one camera. Fig. 1. 3D tracking and catching task.

The Recursive Least Squares (RLS) algorithm is renowned for its rapid convergence but in some scenarios it fails to show swiftness required by several applications. Such failure may result due to different limiting conditions. Gain vector plays an essential role in the performance of RLS algorithm. This paper proposes a modification in Gain vector that results in RLS algorithm performing much b...

Summary This work studies an experimental design problem where the values of a predictor variable, denoted by $x$, are to be determined with goal estimating function $m(x)$, which is observed noise. A linear model fitted but it not assumed that correctly specified. It follows quantity interest best approximation $\ell(x)$. shown in this framework ordinary least squares estimator typically leads...