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The objective of this paper is to analyze the mathematical model of Bessel beamformer with least mean square (LMS) beamforming algorithm using offset quadrature phase shift keying (OQPSK)which is one of the efficient digital modulation techniques. The desired user is placed at an angle 20 degree and all other users as interfering signals in a Rayleigh fading scenario. The performance is judged in...
Howling phenomenon caused by acoustic feedback affects the working of an amplifier system. The adaptive linear prediction proved to be effective for howling suppression. In this paper, we proposed a MATLAB platform that provides a virtual environment for the simulation of adaptive howling suppressor (AHS). We improved the system performance by duplicate adaptation. The duplicate-Least Mean Square...
In this paper, new adaptive algorithms are proposed to improve the performance of the variable step-size LMS (VSSLMS) algorithm when the system is sparse. The first proposed algorithm is the zero-attracting (ZA) VSSLMS. This algorithm outperforms the standard VSSLMS if the system is highly sparse. However, the performance of the ZA-VSSLMS algorithm deteriorates when the sparsity of the system decreases...
In this paper, we present a computationally low com-plex Dead Zone Signed Regressor LMS (DZSRLMS) algorithm, that can be applied to ECG signal in order to remove various artifacts from them. This algorithm enjoys less computational complexity because of the sign present in the algorithm and good filtering capability because of the threshold applied to error signal. As a result it is particularly suitable...
In passive bistatic radar system, since echo signals are polluted seriously by strong direct path interference, adaptive direct path interference cancellation system is introduced to rebuild echo signals. To improve convergence rate, variable step Least Mean Square algorithm is used and a new nonlinear function of step and steady state error is proposed. Using cross ambiguity function, time delay...
Modern advanced hardware technology has made possible the implementation of sophisticated algorithms. The Complex Block Least Mean Square (LMS) algorithm has been widely used in adaptive filtering applications. However, the major drawback of this technique is its dependence on the appropriate choice of the step size. This paper presents the Complex Block Conjugate-gradient LMS algorithm with optimal...
In many system identification applications, the unknown system is characterized by time-varying parameters. Therefore, fast on-line identification is required in order to keep the system stable and improve the control performance. In this paper, we show that the dimensionality of system identification can be dramatically reduced if the unknown system is sparse, in the sense that its parameter set...
In this article we analyze the diffusion normalized least mean square (NLMS) and its set-membership version (SMNLMS) diffusion algorithms in a scenario where sensor nodes are subjected to different noise variances. We show through simulation that the SM-NLMS is a more robust algorithm in such condition, in addition to the provided reduced energy consumption. We also show that, in such context, the...
Frequency selective fading effects in OFDM based high speed wireless communication systems can be highly mitigated by using adaptive antenna arrays. In addition, the use of discrete wavelet transform (DWT) processing in OFDM systems can successfully counter the degrading effect of inter symbol interference (ISI) without the need of cyclic prefix (CP), and hence conserves band width and improves the...
A novel variable step size constant modulus algorithm (VSS-CMA) employing cross correlation between channel output and error signal has been proposed as a solution to the problem of slow convergence of CMA algorithm. The new algorithm resolves the conflict between the convergence rate and low steady state error of the fixed step-size conventional CMA algorithm. Computer simulations have been performed...
A great variety of computer vision tasks, such as rigid/nonrigid structure from motion and photometric stereo, can be unified into the problem of approximating a low-rank data matrix in the presence of missing data and outliers. To improve robustness, the L1-norm measurement has long been recommended. Unfortunately, existing methods usually fail to minimize the L1-based nonconvex objective function...
We have previously established delicate connections between the Voronoi diagram of polynomial roots and their basins of attraction with respect to the Basic Family of iteration functions. We have also previously defined polynomiography, visualization techniques in solving a polynomial equation, resulting in a medium with multidisciplinary applications. Here we describe several novel results and survey...
The proportionate normalized least-mean-square (PNLMS) algorithm with individual activation factors (IAFPNLMS) converges fast when the echo path is highly sparse, and has been used in system identification. Unfortunately, it suffers from slow convergence speed after the fast initial process. To solve the problem, in this paper, the idea of mu-law PNLMS (MPNLMS) algorithm is introduced into the IAFPNLMS...
Adaptive algorithms used in smart antenna systems have been thoroughly studied along the years. Well known algorithms like the LMS (Least Mean Squares) were improved and other more performant algorithms were created. An example is the VSSLMS (Variable Step Size LMS) algorithm, with Kwong and Aboulnasr variants. The LMS class of algorithms uses a reference signal in order to reach convergence. Other...
A convex combination LMS (least mean square) algorithm based on Krylov subspace transform is proposed in this paper. In this approach, impulse response of the unknown system is firstly transformed into Krylov subspace, in which the system structure is changed into sparse. Then an improved proportionate normalized LMS (IPNLMS) algorithm and a variable tap-length normalized LMS (VTNLMS) algorithm are...
In this paper, we propose a novel semi-blind algorithm and a novel transform domain algorithm. The feasibility, stability, complexity and other characteristics of the semi-blind algorithm were analyzed. The novel semi-blind algorithm integrates LMS (Least mean square) and CMA (Constant modulus algorithm). It uses the LMS algorithm, which is simple to implement and not computationally intensive, but...
Nonlinear Adaptive Filtering Techniques for system Identification based on Volterra model are widely used for the time-invariant system. This paper investigates a new implementation for time -varying volterra system identification using LMS adaptive algorithm with variable step-size for the linear and nonlinear part of filters. The purpose of this paper is in twofold. Firstly the minimum mean square...
In this paper, a new efficient adaptive filtering algorithm belonging to the Quasi-Newton (QN) family is proposed. In the new algorithm, the autocorrelation matrix is assumed to be Toeplitz. Due to this assumption, the algorithm can be implemented in the frequency domain using the fast Fourier transform (FFT). The proposed algorithm turns out to be particularly suitable for adaptive channel equalization...
Among the well known issues with CMA equalizers is that they can converge slowly. Some recent work indicates that pre-whitening with an adaptive LMS filter, configured for linear-prediction (LP-CMA), can cause the CMA equalizer to converge faster, for some poor channel cases. While this works well for a high-dispersion channel, when the technique is applied to less problematic channels, convergence...
In this paper, we provide an overview of the major developments in the area of sparse adaptive filters, starting from the celebrated works on PNLMS algorithm and its several variants to more recent approaches that use compressed sensing framework, more specifically, LASSO and basis pursuit or matching pursuit, to develop sparse adaptive algorithms with improved mean square error and tracking properties...
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