determination of mobile localization with time of arrival (toa) signal is a requirement in cellular mobile communication. in some of the previous methods, localization with non-line-of-sight (nlos) paths can lead to large position error. also for simplicity, in most simulations suppose non stationary actual environments as stationary. this paper proposes (residual test + recursive least square)...

This paper solves the problem of designing recursive-least-squares (RLS) lattice (or order-recursive) algorithms for adaptive filters that do not involve tapped-delay-line structures. In particular, an RLS–Laguerre lattice filter is obtained.

Intersymbol interference is an unwanted phenomenon that makes communication less reliable. However, an equalizer can reduce the bad influence of intersymbol interference. In general, my work is to compare different equalizers in various scenarios. In the first part of this paper, I explain the motivation of using equalization in detail, followed by introducing prerequisite knowledge of equaliza...

This paper presents distributed adaptive algorithms based on the conjugate gradient (CG) method for distributed networks. Both incre-mental and diffusion adaptive solutions are all considered. The distributed conventional (CG) and modified CG (MCG) algorithms have an improved performance in terms of mean square error as compared with least-mean square (LMS)-based algorithms and a performance th...

We present a discussion of three adaptive filtering algorithms well known for their one-step termination property, in terms of their relationship with the minimal residual method. These algorithms are the normalized least mean square (NLMS), Affine Projection algorithm (APA) and the recursive least squares algorithm (RLS). The NLMS is shown to be a result of the orthogonality condition imposed ...

In this paper a Fast Array Multichannel Two-Dimensional Recursive Least Square (FAM 2D-RLS) adaptive filter is proposed for estimating an OFDM channel in frequency domain. This filter makes use of the shift structure of the input data vector. Thus the computational cost of the classical RLS filter which is O(M) is reduced to O(M) for each iteration where M is the order of the filter. In order t...

This study investigates the ability of recursive least squares (RLS) and mean square (LMS) adaptive filtering algorithms to predict quickly track unknown systems. Tracking system behavior is important if there are other parallel systems that must follow exactly same at time. The algorithm can correct filter coefficients according changes in parameters minimize errors between output for input si...

Various Adaptive filtering methods are using to filter Cardiac signals. The two basic adaptive filtering algorithms are LMS (Least Mean Square) and RLS (Recursive Least Square). These Adaptive Algorithms are used to filter artifacts from ECG signal. Adaptive filter minimises the mean square error between the primary input, which is ECG with noise and desired response, which is either ECG or noi...

In this work, a recently derived recursive least-square (RLS) algorithm to train multi layer perceptron (MLP) is used in an MLP-based decision feedback equalizer (DFE) instead of the back propagation (BP) algorithm. Its performance is investigated and compared to those of MLP-DFE based on the BP algorithm and the simple DFE based on the least-mean square (LMS) algorithm. The results show improv...

This paper proposes distributed adaptive algorithms based on the conjugate gradient (CG) method and the diffusion strategy for parameter estimation over sensor networks. We present sparsity-aware conventional and modified distributed CG algorithms using l1 and log-sum penalty functions. The proposed sparsity-aware diffusion distributed CG algorithms have an improved performance in terms of mean...