Abstract
Decentralized detection is one of the key tasks that a wireless sensor network (WSN) is faced to accomplish. Among several decision criteria, the Rao test is able to cope with an unknown (but parametrically-specified) sensing model, while keeping computational simplicity. To this end, the Rao test is employed in this paper to fuse multivariate data measured by a set of sensor nodes, each observing the target (or the desired) event via a nonlinear mapping function. In order to meet stringent energy/bandwidth requirements, sensors quantize their vector-valued observations into one or few bits and send them over error-prone (to model low-power communications) reporting channels to a fusion center (FC). Therein, a global (better) decision is taken via the proposed test. Its closed form and asymptotic (large-size WSN) performance are obtained, and the latter leveraged to optimize quantizers. The appeal of the proposed approach is confirmed via simulations.
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