Multi-Layered Recursive Least Squares for Time-Varying System Identification

Multiple Concurrent Recursive Least Squares Identification

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...

Smoothly Time Varying Systems and Least Squares System Identi

Recursive Least Squares Channel Estimation for Rapidly Time-Varying Scenarios in IEEE 802.11p

Rapid time-varying channel estimation is one of the biggest challenges in IEEE 802.11p standard. It is a wireless vehicular communication standard which is used for outdoor applications. This paper proposes a novel decision-directed recursive least squares (RLS) time-domain channel estimation method that utilizes the guard interval of every orthogonal frequency division multiplexing (OFDM) symb...

On Exponentially Weighted Recursive Least Squares for Estimating Time-Varying Parameters

The exponentially weighted recursive least-squares (RLS) has a long history as an algorithm to track timevarying parameters in signal processing and time series analysis. By reviewing the optimality conditions of RLS under a regression framework, possible sources of suboptimality of RLS for tracking time-varying parameters, especially when the parameters satisfy a state-space model, are identif...

Kernel Recursive Least Squares

We present a non-linear kernel-based version of the Recursive Least Squares (RLS) algorithm. Our Kernel-RLS algorithm performs linear regression in the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum meansquared-error regressor. Sparsity (and therefore regularization) of the solution is achieved by an explicit greedy sparsification proces...