Journal ArticleDOI
Optimum Quantization for Local Decision Based on Independent Samples
H.V. Poor,John B. Thomas +1 more
TLDR
Applying the theory of local tests, general criteria are derived for the optimal selection of quantizer parameters for the large-sample-size case and are shown to lead to that quantizer-decision system which is most efficient asymptotically.Abstract:
The problem of designing quantizers for use in decision-making systems is considered. Applying the theory of local tests, general criteria are derived for the optimal selection of quantizer parameters for the large-sample-size case. These criteria agree with previously established results based on optimization in terms of distance measures and are shown also to lead to that quantizer-decision system which is most efficient asymptotically. To illustrate the design procedure, several applications to signal detection are discussed.read more
Citations
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Journal ArticleDOI
Detection in Sensor Networks: The Saddlepoint Approximation
Saeed Aldosari,Jose M. F. Moura +1 more
TL;DR: The paper demonstrates with parallel fusion sensor network problems the accuracy of the saddlepoint methodology: 1) computing the detection performance for a variety of small and large sensor network scenarios; and 2) designing the local detection thresholds.
Journal ArticleDOI
Optimum quantization for detection
B. Picinbono,P. Duvaut +1 more
TL;DR: The problem has been solved for the basic detection situation using the locally optimal procedure and with some constraints on the quantization scheme, and the solution introduces the concept of quantization by a likelihood ratio procedure.
Journal ArticleDOI
On Optimum and Nearly Optimum Data Quantization for Signal Detection
B. Aazhang,H.V. Poor +1 more
TL;DR: The application of companding approximation theory to the quantization of data for detection of coherent signals in a noisy environment is considered, allowing for greater simplicity in both analysis and design of quantizers for detection systems.
Proceedings Article
Asymptotically optimal quantizers for detection of I.I.D. data
G. R. Benitz,J. A. Bucklew +1 more
TL;DR: In this paper, the asymptotic probability of error for quantization in maximum-likelihood tests is analyzed, where quantizers with large numbers of levels generated from a companding function are assumed.
Journal ArticleDOI
Asymptotically optimal quantizers for detection of i.i.d
G.R. Benitz,James A. Bucklew +1 more
TL;DR: In this article, the asymptotic probability of error for quantization in maximum-likelihood tests is analyzed, where quantizers with large numbers of levels generated from a companding function are assumed.
References
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Journal ArticleDOI
Detectors for discrete-time signals in non-Gaussian noise
James H. Miller,John B. Thomas +1 more
TL;DR: The structure and performance of a class of nonlinear detectors for discrete-time signals in additive white noise are investigated and three general classes of symmetric, unimodal, univariate probability density functions are introduced that are generalizations of the Gaussian, Cauchy, and beta distributions.
Journal ArticleDOI
Optimum Quantization for Signal Detection
TL;DR: It is shown that two useful detection criteria lead to quantization which gives the minimum mean-squared error between the quantized output and the locally optimum nonlinear transform for each data sample.
Journal ArticleDOI
On the asmptotic efficiency of locally optimum detectors
TL;DR: The locally optimum detector is shown to be asymptotically as efficient as the Neyman-Pearson detector, and a number of applications to several detection problems are considered.