Power allocation for decision fusion in wireless sensor networks by the Cauchy-Schwartz divergence
| Autor: | Saeed Hakimi |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Similarity (geometry)
business.industry 010401 analytical chemistry 020206 networking & telecommunications Pattern recognition 02 engineering and technology Mixture model 01 natural sciences Measure (mathematics) 0104 chemical sciences Range (mathematics) 0202 electrical engineering electronic engineering information engineering Artificial intelligence Closed-form expression Divergence (statistics) business Algorithm Wireless sensor network Mathematics Statistical signal processing |
| Zdroj: | 2017 Iranian Conference on Electrical Engineering (ICEE). |
| DOI: | 10.1109/iraniancee.2017.7985307 |
| Popis: | The statistical distances or similarity measures are fundamental tools for solving a wide range of statistical signal processing problems. In this paper, we consider a novel information theoretic divergence as a performance criterion to optimize decision fusion over a wireless sensor network. Specifically, the Cauchy-Schwartz divergence between probability densities of the received signal under different hypotheses is used. This measure can lead to an analytic closed form expression for a mixture of Gaussians, while most of the well-known divergences cannot. Both orthogonal and nonorthogonal communication channels are considered. Simulation results validate the theoretically claimed improvement in the performance. |
| Databáze: | OpenAIRE |
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