Антенная решетка и эквалайзер с обратной связью как единое адаптивное устройство

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.

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

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.

Improved Adaptive Beamforming for Smart Antennas

In this paper, we propose a new antenna array configuration for smart antenna beamforming. In this new method, we displace two antenna elements of a uniform linear array (ULA) and place them at the top and bottom of the array axis. We investigate the efficacy of this method by deploying the variable step size least mean square algorithm (VSSLMS). The proposed method is compared with popular LMS and normalized LMS algorithms. Computer simulations show that the proposed method has enhanced convergence rate and high data transmission compared to the LMS and the NLMS methods. Also, the new method has the same performance for middle angles, near boresight and array endfires which is not possible for the LMS and the NLMS method using a ULA.

Bird Species Recognizer using LMS Algorithm

Birds play a vital role in many ecosystems, acting as both predators and preys for other living organisms. Therefore its important to monitor the population of various bird species in the environment in order to maintain balance in the ecosystem. This process will become tedious if it is done manually as it involves handling large sets of data at the same instant. We can do this by developing an automatic bird species recognizer which identifies the bird species based on bird songs and voice signals. In this research, we have used a tenth-order LMS adaptive filter to remove noise from bird voice signals which are recorded in different environmental conditions where different noise frequencies are present. The design of a tenth-order LMS adaptive filter using MATLAB has been implemented. The performance and characteristics of the filter for five different methods of LMS has been shown. After removal of noise from the noisy bird voice signal using LMS algorithm, we have made use of cross correlation to identify the bird species that it corresponds to. Signal to Noise Ratio (SNR) and Mean Square Error (MSE) of the filtered bird signals obtained using the variants of LMS like Normalized LMS, Sign-Data LMS, SignError LMS and Sign-Sign LMS have been estimated and compared. We have made use of signal processing tool kits and various noise parameter schemes have been computed to show the effectiveness of the designed filter in the field of bird recognition.