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Energy-efficient robust acoustic source localization with local outlier rejection in Wireless Sensor Networks

机译：在无线传感器网络中具有本地异常抑制的节能强大声学源定位

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摘要

Outliers can be devastating for conventional MLE and LS based acoustic source localization methods. To solve this problem with less energy consumption is the focus of this paper. A contaminated Gaussian model is proposed for acoustic energy sensor readings and shown to be consistent with real-world experiment data. Then a novel energy-efficient robust acoustic source localization protocol (ERASLP) is introduced where each node examines its own observation by a local outlier rejection rule (LORR). Detected outliers will not be sent to the fusion center for the final localization result calculation. The average communication energy cost and applicability of ERASLP is analyzed and compared to typical centralized localization method and distributed optimization method. Simulation results show LORR can surpass more than a half of the outliers even with very sparsely deployed sensors and ERASLP has better tradeoff between robustness and energy consumption in most cases.
机译：异常值可能会为传统的MLE和LS的声学源定位方法造成损坏。为了解决较少的能源消耗来解决这个问题是本文的重点。提出了一种污染的高斯模型，用于声能传感器读数，并显示与现实世界的实验数据一致。然后引入了一种新颖的节能强大声学源定位协议（ERASLP），其中每个节点通过本地异常值抑制规则（LORR）检查其自己的观察。检测到的异常值不会被发送到融合中心，以获得最终的本地化结果计算。分析了eraslp的平均通信能量成本和适用性，并与典型的集中定位方法和分布式优化方法进行了比较。仿真结果显示Lorr可以超越超过一半的异常值，即使在大多数情况下，Eraslp在鲁棒性和能量消耗之间具有更好的权衡。

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