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• W2116254297 abstract "The problem of detecting a wide-sense stationary Gaussian signal process embedded in white Gaussian noise, where the power spectral density of the signal process exhibits uncertainty, is investigated. The performance of minimax robust detection is characterized by the exponential decay rate of the miss probability under a Neyman-Pearson criterion with a fixed false alarm probability, as the length of the observation interval grows without bound. A dominance condition is identified for the uncertainty set of spectral density functions, and it is established that, under the dominance condition, the resulting minimax problem possesses a saddle point, which is achievable by the likelihood ratio tests matched to a so-called dominated power spectral density in the uncertainty set. No convexity condition on the uncertainty set is required to establish this result." @default.
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• W2116254297 date "2010-06-01" @default.
• W2116254297 modified "2023-09-26" @default.
• W2116254297 title "On minimax robust detection of stationary Gaussian signals in white Gaussian noise" @default.
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• W2116254297 doi "https://doi.org/10.1109/isit.2010.5513375" @default.
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