Asymptotically Robust Detection of Stochastic Signals in Contaminated Noise,

Abstract

We consider the discrete time detection of stochastic signals in white noise, where the univariate noise density is known perfectly only on an interval about the origin. We present a method to enhance the asymptotic performance of the detector by exploiting this knowledge, and at the same time preserve robustness properties of the detector to the remaining inexact knowledge of the univariate noise density via a saddlepoint condition. We then provide examples to show that improved performance is indeed obtained. (Author)

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Document Details

Document Type
Technical Report
Publication Date
May 22, 1986
Accession Number
ADA170165

Entities

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  • D. R. Halverson
  • M. S. Schnitzer

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  • Texas A&M University

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  • Materials and Manufacturing Processes

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