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In this paper, we address the problem of decentralized parameter estimation with a Wireless Sensor Network (WSN). The network is hierarchical in that sensors are grouped into clusters, being each cluster under the supervision of a cluster-head (CH). The CH is also in charge of consolidating the sensor observations into a local estimate before its transmission to the Fusion Center (FC). In this context,...
We derive the Neyman-Pearson error exponent for the detection of Gauss-Markov signals using randomly spaced sensors. We assume that the sensor spacings, d1,d2,..., are drawn independently from a common density fd(.), and we treat both stationary and nonstationary Markov models. Error exponents are evaluated using specialized forms of the strong law of large numbers, and are seen to take on algebraically...
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