文档介绍：Haykin, Simon “Adaptive Systems
for Signal Process"
Advanced Signal Processing Handbook
Editor: Stergios Stergiopoulos
Boca Raton: CRC Press LLC, 2001
2
Adaptive Systems
for Signal Process*
Simon Haykin The Filtering Problem
McMaster University Adaptive Filters
Linear Filter Structures
Transversal Filter • Lattice Predictor • Systolic Array
Approaches to the Development of Linear Adaptive
Filtering Algorithms
Stochastic Gradient Approach • Least-Squares Estimation
• How to Choose an Adaptive Filter
Real plex Forms of Adaptive Filters
Nonlinear Adaptive Systems: works
Supervised Learning • Unsupervised Learning • Information-
Theoretic Models • Temporal Processing Using Feedforward
Networks • Dynamically Driven works
Applications
System Identification • Spectrum Estimation • Signal
Detection • Target Tracking • Adaptive Noise Canceling
• Adaptive Beamforming
Concluding Remarks
References
The Filtering Problem
The term “filter” is often used to describe a device in the form of a piece of physical hardware or software
that is applied to a set of noisy data in order to extract information about a prescribed quantity of interest.
The noise may arise from a variety of sources. For example, the data may have been derived by means
of noisy sensors or may represent a useful ponent that has been corrupted by transmission
through munication channel. In any event, we may use a filter to perform three basic information-
processing tasks.
1. Filtering means the extraction of information about a quantity of interest at time t by using data
measured up to and including time t.
2. Smoothing differs from filtering in that information about the quantity of interest need not be
available at time t, and data measured later than time t can be used in obtaining this information.
This means that in the case of smoothing there is a delay in producing the result of interest. Since