Издательство InTech, 2011, -410 pp.
A digital filter is a structure that transforms sequences of numbers to others from its input to its output (signals) and models thus the behavior of a real system. The model or transfer function is a simplified mathematical representation of the system. The structure of the filter consists of a few elements: delays, multipliers, adders and, less often, functions whose magnitude, combination and number determine its characteristics. An adaptive filter, however, is able to self-adjust the parameters of such elements in time (the coefficients of the multipliers for example) according to certain algorithm and thus the relationship between the input and output sequences to adapt itself to the changes of the complex system that represents. This update takes place, usually, by minimizing a cost function in an iterative scheme. Digital adaptive filters are, therefore, very popular in any implementation of signal processing where the system modelled and/or the input signals are time-variants; such as the echo cancellation, active noise control, blind channel equalization, etc., corresponding to problems of system identification, inverse modeling, prediction, interference cancellation, etc.
Any design of an adaptive filter focuses its attention on some of its components: structure (transversal, recursive, lattice, systolic array, non-linear, transformed domain, etc.), cost function (mean square error, least squares), coefficient update algorithm (no memory, block, gradient, etc.); to get certain benefits: robustness, speed of convergence, misalignment, tracking capacity, computational complexity, delay, etc.
This book is composed of 15 motivating chapters written by researchers and professionals that design, develop and analyze different combinations or variations of the components of the adaptive filter and apply them to different areas of knowledge. The first part of the book is devoted to the adaptive filtering fundamentals and evaluation of their performances while the second part presents structures and complex algorithms in specific applications.
This information is very interesting not only for all those who work with technologies based on adaptive filtering but also for teachers and professionals interested in the digital signal processing in general and in how to deal with the complexity of real systems in particular: non-linear, time-variants, continuous, and unknown.
Part 1 Fundamentals, Convergence, Performance.
Convergence Evaluation of a Random Step-Size NLMS Adaptive Algorithm in System Identification and Channel Equalization.
Steady-State Performance Analyses of Adaptive Filters.
The Ultra High Speed LMS Algorithm Implemented on Parallel Architecture Suitable for Multidimensional Adaptive Filtering.
An LMS Adaptive Filter Using Distributed Arithmetic - Algorithms and Architectures.
Part 2 Complex Structures, Applications and Algorithms.
Adaptive Filtering Using Subband Processing: Application to Background Noise Cancellation.
Hirschman Optimal Transform (HOT) DFT Block LMS Algorithm.
Real-Time Noise Cancelling Approach on Innovations-Based Whitening Application to Adaptive FIR RLS in Beamforming Structure.
Adaptive Fuzzy Neural Filtering for Decision Feedback Equalization and Multi-Antenna Systems.
A Stereo Acoustic Echo Canceller Using Cross-Channel Correlation.
EEG-fMRI Fusion:
Adaptations of the Kalman Filter for Solving a High-Dimensional Spatio-Temporal Inverse Problem.
Adaptive-FRESH Filtering.
Transient Analysis of a Combination of Two Adaptive Filters.
Adaptive Harmonic IIR Notch Filters for Frequency Estimation and Tracking.
Echo Cancellation for Hands-Free Systems.
Adaptive Heterodyne Filters.
A digital filter is a structure that transforms sequences of numbers to others from its input to its output (signals) and models thus the behavior of a real system. The model or transfer function is a simplified mathematical representation of the system. The structure of the filter consists of a few elements: delays, multipliers, adders and, less often, functions whose magnitude, combination and number determine its characteristics. An adaptive filter, however, is able to self-adjust the parameters of such elements in time (the coefficients of the multipliers for example) according to certain algorithm and thus the relationship between the input and output sequences to adapt itself to the changes of the complex system that represents. This update takes place, usually, by minimizing a cost function in an iterative scheme. Digital adaptive filters are, therefore, very popular in any implementation of signal processing where the system modelled and/or the input signals are time-variants; such as the echo cancellation, active noise control, blind channel equalization, etc., corresponding to problems of system identification, inverse modeling, prediction, interference cancellation, etc.
Any design of an adaptive filter focuses its attention on some of its components: structure (transversal, recursive, lattice, systolic array, non-linear, transformed domain, etc.), cost function (mean square error, least squares), coefficient update algorithm (no memory, block, gradient, etc.); to get certain benefits: robustness, speed of convergence, misalignment, tracking capacity, computational complexity, delay, etc.
This book is composed of 15 motivating chapters written by researchers and professionals that design, develop and analyze different combinations or variations of the components of the adaptive filter and apply them to different areas of knowledge. The first part of the book is devoted to the adaptive filtering fundamentals and evaluation of their performances while the second part presents structures and complex algorithms in specific applications.
This information is very interesting not only for all those who work with technologies based on adaptive filtering but also for teachers and professionals interested in the digital signal processing in general and in how to deal with the complexity of real systems in particular: non-linear, time-variants, continuous, and unknown.
Part 1 Fundamentals, Convergence, Performance.
Convergence Evaluation of a Random Step-Size NLMS Adaptive Algorithm in System Identification and Channel Equalization.
Steady-State Performance Analyses of Adaptive Filters.
The Ultra High Speed LMS Algorithm Implemented on Parallel Architecture Suitable for Multidimensional Adaptive Filtering.
An LMS Adaptive Filter Using Distributed Arithmetic - Algorithms and Architectures.
Part 2 Complex Structures, Applications and Algorithms.
Adaptive Filtering Using Subband Processing: Application to Background Noise Cancellation.
Hirschman Optimal Transform (HOT) DFT Block LMS Algorithm.
Real-Time Noise Cancelling Approach on Innovations-Based Whitening Application to Adaptive FIR RLS in Beamforming Structure.
Adaptive Fuzzy Neural Filtering for Decision Feedback Equalization and Multi-Antenna Systems.
A Stereo Acoustic Echo Canceller Using Cross-Channel Correlation.
EEG-fMRI Fusion:
Adaptations of the Kalman Filter for Solving a High-Dimensional Spatio-Temporal Inverse Problem.
Adaptive-FRESH Filtering.
Transient Analysis of a Combination of Two Adaptive Filters.
Adaptive Harmonic IIR Notch Filters for Frequency Estimation and Tracking.
Echo Cancellation for Hands-Free Systems.
Adaptive Heterodyne Filters.