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.

中文翻译：

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通过多传感器融合进行带宽受限的未知矢量信号的分散检测

分散检测是无线传感器网络（WSN）所要完成的关键任务之一。在多个决策标准中，Rao检验能够处理未知（但参数指定）的传感模型，同时保持计算的简便性。为此，本文采用Rao检验融合由一组传感器节点测量的多元数据，每个传感器节点均通过非线性映射函数观察目标（或所需）事件。为了满足严格的能量/带宽要求，传感器将其矢量值观测值量化为一位或几位，然后通过易于出错的（以建模低功率通信方式）报告通道将其发送到融合中心（FC）。其中，通过提议的测试做出了一个全局（更好）的决策。获得其封闭形式和渐近（大型WSN）性能，后者则用于优化量化器。通过仿真证实了所提出方法的吸引力。