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Generalized Locally Most Powerful Tests for Distributed Sparse Signal Detection
IEEE Transactions on Signal and Information Processing over Networks ( IF 3.2 ) Pub Date : 2022-06-07 , DOI: 10.1109/tsipn.2022.3180682
Abdolreza Mohammadi 1 , Domenico Ciuonzo 2 , Ali Khazaee 1 , Pierluigi Salvo Rossi 3
Affiliation  

In this paper we tackle distributed detection of a localized phenomenon of interest (POI) whose signature is sparse via a wireless sensor network. We assume that both the position and the emitted power of the POI are unknown, other than the sparsity degree associated to its signature. We consider two communication scenarios in which sensors send either ($i$) their compressed observations or ($ii$) a 1-bit quantization of them to the fusion center (FC). In the latter case, we consider non-ideal reporting channels between the sensors and the FC. We derive generalized (i.e. based on Davies’ framework (Davies, 1977)) locally most powerful detectors for the considered problem with the aim of obtaining computationally-efficient fusion rules. Moreover, we obtain their asymptotic performance and, based on such result, we design the local quantization thresholds at the sensors by solving a 1-D optimization problem. Simulation results confirm the effectiveness of the proposed design and highlight only negligible performance loss with respect to counterparts based on the (more-complex) generalized likelihood ratio.

中文翻译:

分布式稀疏信号检测的广义局部最强大测试

在本文中,我们通过无线传感器网络解决了签名稀疏的局部感兴趣现象 (POI) 的分布式检测问题。我们假设除了与其特征相关的稀疏度之外,POI 的位置和发射功率都是未知的。我们考虑两种通信场景,其中传感器发送($i$)他们的压缩观察或($ii$) 对融合中心 (FC) 进行 1 位量化。在后一种情况下,我们考虑传感器和 FC 之间的非理想报告通道。我们为所考虑的问题推导了广义的(即基于戴维斯的框架(Davies,1977))局部最强大的检测器,目的是获得计算效率高的融合规则。此外,我们获得了它们的渐近性能,并基于这样的结果,我们通过解决一维优化问题来设计传感器处的局部量化阈值。仿真结果证实了所提出设计的有效性,并且仅强调了相对于基于(更复杂)广义似然比的对应物的可忽略不计的性能损失。
更新日期:2022-06-07
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