descargar Partial Update LeastSquare Adaptive Filtering en PDF
Bei Xie
Descripción
Sinopsis
Adaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity ($O(N)$) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster and have lower steadystate mean square error (MSE) than LMS. However, their high computational complexity ($O(N^2)$) makes them unsuitable for many realtime applications. A wellknown approach to controlling computational complexity is applying partial update (PU) method to adaptive filters. A partial update method can reduce the adaptive algorithm complexity by updating part of the weight vector instead of the entire vector or by updating part of the time. In the literature, there are only a few analyses of these partial update adaptive filter algorithms. Most analyses are based on partial update LMS and its variants. Only a few papers have addressed partial update RLS and Affine Projection (AP). Therefore, analyses for PU leastsquares adaptive filter algorithms are necessary and meaningful.This monograph mostly focuses on the analyses of the partial update leastsquares adaptive filter algorithms. Basic partial update methods are applied to adaptive filter algorithms including Least Squares CMA (LSCMA), EDS, and CG. The PU methods are also applied to CMA12 and NCMA to compare with the performance of the LSCMA. Mathematical derivation and performance analysis are provided including convergence condition, steadystate mean and meansquare performance for a timeinvariant system. The steadystate mean and meansquare performance are also presented for a timevarying system. Computational complexity is calculated for each adaptive filter algorithm. Numerical examples are shown to compare the computational complexity of the PU adaptive filters with the fullupdate filters. Computer simulation examples, including system identification and channel equalization, are used to demonstrate the mathematical analysis and show the performance of PU adaptive filter algorithms. They also show the convergence performance of PU adaptive filters. The performance is compared between the original adaptive filter algorithms and different partialupdate methods. The performance is also compared among similar PU leastsquares adaptive filter algorithms, such as PU RLS, PU CG, and PU EDS. In addition to the generic applications of system identification and channel equalization, two special applications of using partial update adaptive filters are also presented. One application uses PU adaptive filters to detect Global System for Mobile Communication (GSM) signals in a local GSM system using the Open Base Transceiver Station (OpenBTS) and Asterisk Private Branch Exchange (PBX). The other application uses PU adaptive filters to do image compression in a system combining hyperspectral image compression and classification.
Acerca de Bei Xie
Bei Xie received a Ph.D. in electrical engineering from Virginia Polytechnic Institute and State University in 2012. Her interests include signal processing and communications.
Acerca de Tamal Bose
Dr. Tamal Bose serves as Professor and Department Head of Electrical and Computer Engineering at the University of Arizona. He is also the Director of a multiuniversity NSF Center called the Broadband Wireless Access & Applications Center (BWAC).Dr. Bose’s research interests include signal classification for cognitive radios, channel equalization, adaptive filtering algorithms, and nonlinear effects in digital filters. He is author of the text Digital Signal and Image Processing, John Wiley, 2004, and coauthor of Basic Simulation Models of Phase Tracking Devices Using MATLAB, Morgan & Claypool Publishers, 2010.
ISBN: 9781627052320
Idioma: Español
Formatos: pdf epub kindle mobi
$2376.00
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