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Journal ArticleDOI

Performance of radial-basis function networks for direction of arrival estimation with antenna arrays

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TLDR
This paper discusses the application of a three-layer radial-basis function neural network (RBFNN), which can learn multiple source-direction findings of a six-element array and shows that the RBFNN substantially reduced the CPU time for the DOA estimation computations.
Abstract
The problem of direction of arrival (DOA) estimation of mobile users using linear antenna arrays is addressed. To reduce the computational complexity of superresolution algorithms, e.g. multiple signal classification (MUSIC), the DOA problem is approached as a mapping which can be modeled using a suitable artificial neural network trained with input output pairs. This paper discusses the application of a three-layer radial-basis function neural network (RBFNN), which can learn multiple source-direction findings of a six-element array. The network weights are modified using the normalized cumulative delta rule. The performance of this network is compared to that of the MUSIC algorithm for both uncorrelated and correlated signals. It is also shown that the RBFNN substantially reduced the CPU time for the DOA estimation computations.

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Citations
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A neural network-based smart antenna for multiple source tracking

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Machine learning for wireless communications in the Internet of Things: A comprehensive survey

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Learning-by-examples techniques as applied to electromagnetics

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A smart antenna system for direction of arrival estimation based on a support vector regression

TL;DR: An efficient method based on the support vector regression is proposed, in which the mapping among the outputs of the array and the DOAs of unknown plane waves is approximated by means of a family of support vector machines.
References
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Book

Neural Networks: A Comprehensive Foundation

Simon Haykin
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal ArticleDOI

Multiple emitter location and signal parameter estimation

TL;DR: In this article, a description of the multiple signal classification (MUSIC) algorithm, which provides asymptotically unbiased estimates of 1) number of incident wavefronts present; 2) directions of arrival (DOA) (or emitter locations); 3) strengths and cross correlations among the incident waveforms; 4) noise/interference strength.
Journal ArticleDOI

Fast learning in networks of locally-tuned processing units

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).
Journal ArticleDOI

Detection of signals by information theoretic criteria

TL;DR: Simulation results that illustrate the performance of the new method for the detection of the number of signals received by a sensor array are presented.
Book

Introduction to Adaptive Arrays

TL;DR: This second edition is an extensive modernization of the bestselling introduction to the subject of adaptive array sensor systems, taking the reader by the hand and leading them through the maze of jargon that often surrounds this highly technical subject.
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