Journal ArticleDOI
Detection and estimation using an adaptive rational function filter
H. Leung,Simon Haykin +1 more
TLDR
The adaptive rational function filter is proposed, a new nonlinear adaptive filter structure based on rational functions that is suitable for real-time adaptive signal processing and has a best approximation for a specified function.Abstract:
Proposes a new nonlinear adaptive filter structure based on rational functions. There are several advantages to the use of this filter. First, it is a universal approximator and a good extrapolator. Second, it ran be trained by a linear adaptive algorithm, which makes it suitable for real-time adaptive signal processing. Third, it has a best approximation for a specified function. To demonstrate its utility as a tool for solving adaptive signal processing problems, the authors apply the adaptive rational function filter to the problem of estimation and detection. The estimation problem pertains to the direction of arrival (DOA) estimation problem in array signal processing. For the detection problem, the authors consider the detection of a weak radar target (a small piece of ice) in an ocean environment. >read more
Citations
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Journal ArticleDOI
Prediction of noisy chaotic time series using an optimal radial basis function neural network
Henry Leung,Titus Lo,Sichun Wang +2 more
TL;DR: This paper considers the problem of optimum prediction of noisy chaotic time series using a basis function neural network, in particular, the radial basis function (RBF) network and proposes a new technique called the cross-validated subspace method to estimate the optimum number of hidden units.
Journal ArticleDOI
Detection of small objects in clutter using a GA-RBF neural network
Henry Leung,N. Dubash,N. Xie +2 more
TL;DR: It is shown here that if the functional form of an unknown nonlinear dynamical system can be represented exactly using an RBF net, this GA-RBF approach can reconstruct the exact dynamic from its time series measurements.
Journal ArticleDOI
Selected list of references on radar signal processing
TL;DR: In this article, the authors collected almost 700 references in a single document to facilitate their work and the work of other colleagues of the radar community, and the collection of references is by no means exhaustive; the period of screening mainly covers the last two decades.
Journal ArticleDOI
The rational filter for image smoothing
TL;DR: A nonlinear operator is presented that is able to effectively attenuate the noise that corrupts an image while introducing small distortions on the image details.
Journal ArticleDOI
Highlights of statistical signal and array processing
Alfred O. Hero,Hagit Messer,J. Goldberg,David J. Thomson,M.G. Amin,Georgios B. Giannakis,Ananthram Swami,Jitendra K. Tugnait,Arye Nehorai,A.L. Swindlehurst,Jean-François Cardoso,Lang Tong,Jeffrey L. Krolik +12 more
TL;DR: This article represents an endeavor by the members of the SSAT-TC to review all the significant developments in the field of SSAP and introduces the recent reorganization of three technical committees of the Signal Processing Society.
References
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Journal ArticleDOI
Fast learning in networks of locally-tuned processing units
John Moody,Christian J. Darken +1 more
TL;DR: This work proposes a network architecture which uses a single internal layer of locally-tuned processing units to learn both classification tasks and real-valued function approximations (Moody and Darken 1988).
BookDOI
Nonlinear Digital Filters
TL;DR: This chapter discusses digital filters based on order statistics, Morphological image and signal processing, and Adaptive nonlinear filters.
Book
Nonlinear Digital Filters : Principles and Applications
TL;DR: In this paper, the authors present a survey of algorithms and architectures for image and signal processing based on order statistics and homomorphies, including adaptive nonlinear filters and median filters.
Book
Array Signal Processing
TL;DR: The author explains the development of the Wiener Solution and some of the techniques used in its implementation, including Optimum Processing: Steady State Performance and theWiener Solution, which simplifies the implementation of the Covariance Matrix.
Journal ArticleDOI
Networks and the Best Approximation Property
Federico Girosi,Tomaso Poggio +1 more
TL;DR: The main result of this paper is that multilayer perceptron networks, of the type used in backpropagation, do not have the best approximation property and it is proved that networks derived from regularization theory and including Radial Basis Functions, have a similar property.