We present a methodology for adaptive ltering and system identi cation under the cyclostationary regime. Our technique is based on a deterministic periodic least-squares criterion, and gives rise to adaptive periodic recursive-least-squares (P-RLS) algorithms. Furthermore, we show that every adaptive RLS algorithm has a P-RLS counterpart, which has exactly the same architecture and the same per...

Recently developed recursive least squares schemes, where the square root of both the covariance and the information matrix are stored and updated, are known to be particularly suited for parallel implementation. However, when finite precision arithmetic is used, round-off errors apparently accumulate unboundedly, so that after a number of updates the computed least squares solutions turn out t...

A novel noncoherent receiver for M{ary di erential phase shift{keying (MDPSK) signals transmitted over intersymbol interference (ISI) channels is presented. The noncoherent receiver consists of a linear equalizer and a decision{feedback di erential detector. A signi cant performance gain over a previously proposed noncoherent receiver can be observed. For an in nite number of feedback symbols, ...

In this paper we present conventional and translation-invariant (TI) wavelet-based approaches for single-trial evoked potential estimation based on intracortical recordings. We demonstrate that the wavelet-based approaches outperform several existing methods including the Wiener filter, least mean square (LMS), and recursive least squares (RLS), and that the TI wavelet-based estimates have high...

An approximation for the constant modulus (CM) cost function is proposed to allow the use of the fast recursive least squares (RLS) algorithm. Simulations are performed to compare the performance of the introduced RLS-CM and stochastic gradient descent (SGD) algorithms for blind adaptive beamforming. Results indicate that the introduced RLS-CM has faster convergence speed and good tracking abil...

This paper designs the recursive least-squares (RLS) Wiener finite impulse response (FIR) predictor and filter, based on the innovation approach, in linear discrete-time stochastic systems. It is assumed that the signal is observed with additive white noise and the signal process is uncorrelated with the observation noise process. This paper also presents the recursive algorithms for the estima...

| In this paper we exploit the one-to-one correspondences between the recursive least-squares (RLS) and Kalman variables to formulate extended forms of the RLS algorithm. Two particular forms of the extended RLS algorithm are considered, one pertaining to a system identiication problem and the other pertaining to the tracking of a chirped sinusoid in additive noise. For both of these applicatio...

a r t i c l e i n f o a b s t r a c t Keywords: EEG/ERP Adaptive filter SNR LMS RLS ABC In this paper, the Artificial Bee Colony (ABC) algorithm is applied to construct Adaptive Noise Canceller (ANC) for electroencephalogram (EEG)/Event Related Potential (ERP) filtering with modified range selection, described as Bounded Range ABC (BR-ABC). ERP generated due to hand movement is filtered through...

An online nonnegative matrix factorization (NMF) algorithm based on recursive least squares (RLS) is described in a matrix form, and a simplified algorithm for a low-complexity calculation is developed for frame-by-frame online audio source separation system. First, the online NMF algorithm based on the RLS method is described as solving the NMF problem recursively. Next, a simplified algorithm...

Recursive Least Squares (RLS) algorithms have wide-spread applications in many areas, such as real-time signal processing, control and communications. This paper shows that the unique solutions to linear-equality constrained and the unconstrained LS problems, respectively, always have exactly the same recursive form. Their only difference lies in the initial values. Based on this, a recursive a...