| Paper: | SPTM-P13.7 | ||
| Session: | Detection and Classification | ||
| Time: | Friday, May 21, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: Detection, Estimation, and Class. Thry & Apps. | ||
| Title: | ASYMPTOTIC LOCALLY OPTIMAL DETECTION FOR LARGE SCALE SENSOR NETWORKS UNDER THE POISSON REGIME | ||
| Authors: | Youngchul Sung; Cornell University | ||
| Lang Tong; Cornell University | |||
| Ananthram Swami; Army Research Laboratory | |||
| Abstract: | We consider distributed detection with alarge number of identical sensors deployed over a region where thephenomenon of interest (POI) has unknown spatially varying strength.Each sensor makes a decision based on its own measurement ofthe signal at its location and the local decision of each sensor issent to a fusion center through a multiple access channel. The fusioncenter decides whether the POI has occurred in theregion, under a global size constraint in the Neyman-Pearson formulation.Assuming that the initial distribution of sensors is a homogeneousspatial Poisson process, we show that the Poisson process of `alarmed'sensors satisfies the locally asymptotic normality (LAN) condition asthe number of sensor goes to infinity. We derive a new asymptoticallylocally most powerful (ALMP) detector jointly over the fusion schemeand the sensor threshold.We also derivethe conditions on the spatial signal shape to guarantee the existence of the ALMP detector. We show that the optimal test statistic is a weighted sum of localdecisions, the optimal weight function being the shape of the spatialsignal, but the exact value of the spatial signal is not required.The optimal threshold for a single sensor is also derived. Forthe case of independent, identical distributed (i.i.d.) sensorobservation, we show that the counting-based detector is alsoasymptotic locally optimal | ||
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