scholarly journals Антенная решетка и эквалайзер с обратной связью как единое адаптивное устройство

2021 ◽  
Vol 64 (9) ◽  
pp. 550-562
Author(s):  
Виктор Иванович Джиган
Keyword(s):  
Matrix Inversion ◽  
Mean Square ◽  
Least Mean Square ◽  
Least Mean Squares ◽  
Affine Projection ◽  
Matrix Inversion Lemma ◽  
Normalized Lms

В статье рассмотрена адаптивная антенная решетка (ААР), весовые коэффициенты которой совмещены с весовыми коэффициентами части эквалайзера без обратной связи, а выходной сигнал комбинируется с выходным сигналом части эквалайзера с обратной связью. Такие решетка и распределенный эквалайзер функционируют как единый многоканальный адаптивный фильтр, обеспечивающий прием полезного сигнала в условиях его многолучевости и наличия сигналов источников внешних помех. Представлены архитектура антенной решетки/эквалайзера, математическое описание многоканальных адаптивных алгоритмов его работы: рекурсивного алгоритма по критерию наименьших квадратов RLS (Recursive Least Mean Squares) на основе леммы об обращении матрицы MIL (Matrix Inversion Lemma), QR-разложения и преобразования Хаусхолдера с квадратичной вычислительной сложностью, а также простых алгоритмов по критерию наименьшего квадрата LMS (Least Mean Square), нормализованного LMS-алгоритма NLMS (Normalized LMS) и алгоритма аффинных проекций AP (Affine Projection) с линейной вычислительной сложностью. Результаты моделирования линейной антенной решетки с числом антенн/каналов, равным восьми, принимающей полезный сигнал 16-PSK, прошедший через двухлучевой канал связи, при наличии от одного до четырех источников помех с отношением сигнал–помеха (ОСП) –30 дБ по каждой помехе, при отношении сигнал–шум (ОСШ) в каналах решетки 10–30 дБ, демонстрируют эффективность предлагаемого решения.

scholarly journals Multichannel Active Noise Control Based on Filtered-x Affine Projection-Like and LMS Algorithms with Switching Filter Selection

2019 ◽  
Vol 9 (21) ◽  
pp. 4669 ◽  
Author(s):  
Ángel A. Vázquez ◽  
Eduardo Pichardo ◽  
Juan G. Avalos ◽  
Giovanny Sánchez ◽  
Hugo M. Martínez ◽  
...  
Keyword(s):  
Steady State ◽  
Computational Cost ◽  
Lms Algorithm ◽  
Alternative Methods ◽  
Mean Square ◽  
Least Mean Square ◽  
Affine Projection ◽  
Mean Square Errors ◽  
Switching Filter ◽  
Filter Selection

Affine projection (AP) algorithms have been demonstrated to have faster convergence speeds than the conventional least mean square (LMS) algorithms. However, LMS algorithms exhibit smaller steady-state mean square errors (MSEs) when compared with affine projection (AP) algorithms. Recently, several authors have proposed alternative methods based on convex combinations to improve the steady-state MSE of AP algorithms, even with the increased computational cost from the simultaneous use of two filters. In this paper, we present an alternative method based on an affine projection-like (APL-I) algorithm and least mean square (LMS) algorithm to solve the ANC under stationary Gaussian noise environments. In particular, we propose a switching filter selection criteria to improve the steady-state MSE without increasing the computational cost when compared with existing models. Here, we validate the proposed strategy in a single and a multichannel system, with and without automatically adjusting the scaling factor of the APL-I algorithm. The results demonstrate that the proposed scheme exploits the best features of each filter (APL-I and LMS) to guarantee rapid convergence with a low steady-state MSE. Additionally, the proposed approach demands a low computational burden compared with existing convex combination approaches, which will potentially lead to the development of real-time ANC applications.

scholarly journals Adaptive Equalizer Using Selective Partial Update Algorithm and Selective Regressor Affine Projection Algorithm over Shallow Water Acoustic Channels

2014 ◽  
Vol 2014 ◽  
pp. 1-5
Author(s):  
Masoumeh Soflaei ◽  
Paeiz Azmi
Keyword(s):  
Shallow Water ◽  
Water Channel ◽  
Lms Algorithm ◽  
Projection Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Adaptive Equalizer ◽  
Affine Projection ◽  
The Family ◽  
Reliable Communications

One of the most important problems of reliable communications in shallow water channels is intersymbol interference (ISI) which is due to scattering from surface and reflecting from bottom. Using adaptive equalizers in receiver is one of the best suggested ways for overcoming this problem. In this paper, we apply the family of selective regressor affine projection algorithms (SR-APA) and the family of selective partial update APA (SPU-APA) which have low computational complexity that is one of the important factors that influences adaptive equalizer performance. We apply experimental data from Strait of Hormuz for examining the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error (MSE) of SR-APA and SPU-APA decrease by 5.8 (dB) and 5.5 (dB), respectively, in comparison with least mean square (LMS) algorithm. Also the families of SPU-APA and SR-APA have better convergence speed than LMS type algorithm.

An ALO Optimized Adaline Based Controller for an Isolated Wind Power Harnessing Unit

Designs ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 65
Author(s):  
Amritha Kodakkal ◽  
Rajagopal Veramalla ◽  
Narasimha Raju Kuthuri ◽  
Surender Reddy Salkuti
Keyword(s):  
Wind Power ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Step Size ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Normalized Lms

A power generating system should be able to generate and feed quality power to the loads which are connected to it. This paper suggests a very efficient controlling technique, supported by an effective optimization method, for the control of voltage and frequency of the electrical output of an isolated wind power harnessing unit. The wind power unit is modelled using MATLAB/SIMULINK. The Leaky least mean square algorithm with a step size is used by the proposed controller. The Least Mean Square (LMS) algorithm is of adaptive type, which works on the online modification of the weights. LMS algorithm tunes the filter coefficients such that the mean square value of the error is the least. This avoids the use of a low pass filter to clean the voltage and current signals which makes the algorithm simpler. An adaptive algorithm which is generally used in signal processing is applied in power system applications and the process is further simplified by using optimization techniques. That makes the proposed method very unique. Normalized LMS algorithm suffers from drift problem. The Leaky factor is included to solve the drift in the parameters which is considered as a disadvantage in the normalized LMS algorithm. The selection of suitable values of leaky factor and the step size will help in improving the speed of convergence, reducing the steady-state error and improving the stability of the system. In this study, the leaky factor, step size and controller gains are optimized by using optimization techniques. The optimization has made the process of controller tuning very easy, which otherwise was carried out by the trial-and-error method. Different techniques were used for the optimization and on result comparison, the Antlion algorithm is found to be the most effective. The controller efficiency is tested for loads that are linear and nonlinear and for varying wind speeds. It is found that the controller is very efficient in maintaining the system parameters under normal and faulty conditions. The simulated results are validated experimentally by using dSpace 1104. The laboratory results further confirm the efficiency of the proposed controller.

A Study of a various Acoustic Beamforming Techniques Using a Microphone Array

2015 ◽  
Vol 1 ◽  
pp. 7 ◽  
Author(s):  
Ahmed Abdalla ◽  
Suhad Mohammed ◽  
Abdelazeim Abdalla ◽  
Tang Bin ◽  
Mohammed Ramadan
Keyword(s):  
Signal To Noise Ratio ◽  
Microphone Array ◽  
Spatial Filtering ◽  
Mean Square ◽  
Least Mean Square ◽  
Signal To Noise ◽  
Least Mean Squares ◽  
Lms Filter ◽  
Least Mean Square Algorithm ◽  
Simulation Results

In this paper, A study of numerous acoustic beamforming algorithms is carried out. Beamforming algorithms are techniques utilize to determine the Direction of arrival of (DOA) the speech signals while suppress out the corresponding noises and interferences. The simple delay and sum beamformer technique which use the constrained least mean squares (LMS) filter for spatial filtering is firstly investigated. Secondly, a constrained least mean square algorithm (also known as Frost Beamformer) is considered. The beamformer algorithms are simulated in MATLAB and therefore, the simulation results indicate that there a significant enhancement in the Signal-to-Noise-Ratio (SNR) for frost beamformer as compared to the simple delay and sum beamformer.

Adaptive Filters

2011 ◽  
pp. 190-224
Author(s):  
Sergio L. Netto ◽  
Luiz W.P. Biscainho
Keyword(s):  
Adaptive Filters ◽  
Adaptive Signal Processing ◽  
Recursive Least Squares ◽  
Least Mean Square ◽  
Affine Projection ◽  
Signal Prediction ◽  
Iir Filter ◽  
Adaptive Signal ◽  
Filter Algorithms ◽  
Normalized Lms

This chapter focuses on the main aspects of adaptive signal processing. The basic concepts are introduced in a simple framework, and its main applications (namely system identification, channel equalization, signal prediction, and noise cancellation) are briefly presented. Several adaptive algorithms are presented, and their convergence behaviors are analyzed. The algorithms considered in this chapter include the popular least-mean square (LMS), its normalized-LMS version, the affine-projection with the set-membership variation, the recursive least-squares (RLS), the transform-domain, the sub-band domain, and some IIR-filter algorithms such as the equation-error (EE) and the output-error (OE) algorithms. The main purpose of all this presentation is to give general guidelines for the reader to choose the most adequate technique for the audio application at hand.

scholarly journals Set-Membership Quaternion Normalized LMS Algorithm

2018 ◽  
Author(s):  
Hamid Reza Moradi ◽  
Akram Zardadi
Keyword(s):  
Wind Profile ◽  
Adaptive Beamforming ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Step Size ◽  
Set Membership ◽  
Normalized Least Mean Square ◽  
Update Rate ◽  
Normalized Lms

In this paper, we propose the set-membership quaternion normalized least-mean-square (SM-QNLMS) algorithm. For this purpose, first, we review the quaternion least-mean-square (QLMS) algorithm, then go into the quaternion normalized least-mean-square (QNLMS) algorithm. By having the QNLMS algorithm, we propose the SM-QNLMS algorithm in order to reduce the update rate of the QNLMS algorithm and avoid updating the system parameters when there is not enough innovation in upcoming data. Moreover, the SM-QNLMS algorithm, thanks to the time-varying step-size, has higher convergence rate as compared to the QNLMS algorithm. Finally, the proposed algorithm is utilized in wind profile prediction and quaternionic adaptive beamforming. The simulation results demonstrate that the SM-QNLMS algorithm outperforms the QNLMS algorithm and it has higher convergence speed and lower update rate.

Sign in / Sign up

Export Citation Format

Share Document