Maximum Likelihood Wireless Sensor Network Source Localization Using Acoustic Signal Energy Measurements

作者： Xiaohong Sheng

DOI:

关键词: Source localization 、 Search algorithm 、 Algorithm 、 Computation 、 Multiple source localization 、 Sensor field 、 Wireless sensor network 、 Pattern recognition 、 Artificial intelligence 、 Computer science 、 Maximum likelihood 、 Iterative method

摘要: A maximum likelihood (ML) acoustic source location estimation method is presented. This uses signal energy measurements taken at individual sensors of an ad hoc wireless sensor network to estimate the locations multiple sources. Compared existing based localization methods, this proposed ML delivers more accurate results and offers enhanced capability localization. multi-resolution search algorithm expectationmaximization (EM) like iterative are expediate computation locations. The Cramer-Rao Bound (CRB) has been derived. When there only a single in field, corresponding CRB formulation can be used analyze impacts placement accuracy estimates. Extensive simulations have conducted. Empirically, it observed that consistently outperforms methods. An example applying track military vehicles using real world experiment data also demonstrates performance advantage over previously method.

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Maximum Likelihood Wireless Sensor Network Source Localization Using Acoustic Signal Energy Measurements

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Maximum Likelihood Wireless Sensor Network Source Localization Using Acoustic Signal Energy Measurements

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