Elements of information theory
Elements of information theory
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
A competitive Neyman-Pearson approach to universal hypothesis testing with applications
IEEE Transactions on Information Theory
Neyman-pearson detection of gauss-Markov signals in noise: closed-form error exponentand properties
IEEE Transactions on Information Theory
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We study the target class detection performance of a wireless sensor network with a structured node topology. The target is assumed to be in the far-field of the network and positioned at an angle θ which may be known or unknown. The target produces a random signal field that is spatially correlated and dependent on θ and the target's class i, i 2 ∈ {0, 1}. We study the Neyman-Pearson detection error exponent for this scenario using large deviations theory. When θ is known, we derive a closed-form analytic expression for the probability of miss error exponent and show that it is monotonically decreasing in the node spacing d and bounded as d → 0. When θ is unknown, we study its estimation using the Generalized Likelihood Ratio Test (GLRT). We study the error exponent of the GLRT using both analytic techniques and numerical simulations.