We consider decentralized detection of an unknown signal corrupted by zero-mean unimodal noise via wireless sensor networks. We assume the presence of both smart and dumb sensors: the former transmit unquantized measurements, whereas the latter employ multilevel quantizations (before transmission through binary symmetric channels) in order to cope with energy and/or bandwidth constraints. The data are received by a fusion center, which relies on a proposed Rao test, as a simpler alternative to the generalized likelihood ratio test (GLRT). The asymptotic performance analysis of the multibit Rao test is provided and exploited to propose a (signal-independent) quantizer design approach by maximizing the noncentrality parameter of the test-statistic distribution. Since the latter is a nonlinear and nonconvex function of the quantization thresholds, we employ the particle swarm optimization algorithm for its maximization. Numerical results are provided to show the effectiveness of the Rao test in comparison to the GLRT and the boost in performance obtained by (multiple) threshold optimization. Asymptotic performance is also exploited to define detection gain measures allowing to assess gain arising from use of dumb sensors and increasing their quantization resolution.

中文翻译：

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基于Rao测试的融合智哑传感器的多位分散检测

我们考虑通过无线传感器网络分散检测被零均值单峰噪声破坏的未知信号。我们假设同时存在智能和哑传感器：前者传输未量化的测量值，而后者采用多级量化（在通过二进制对称信道传输之前）以应对能量和/或带宽限制。数据由融合中心接收，该中心依赖于提议的 Rao 测试，作为广义似然比测试 (GLRT) 的更简单替代方案。提供并利用多位 Rao 测试的渐近性能分析，通过最大化测试统计分布的非中心参数来提出（信号无关）量化器设计方法。由于后者是量化阈值的非线性和非凸函数，我们采用粒子群优化算法使其最大化。提供了数值结果以显示 Rao 测试与 GLRT 相比的有效性以及通过（多个）阈值优化获得的性能提升。渐近性能也被用来定义检测增益度量，允许评估使用哑传感器产生的增益并增加其量化分辨率。