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Detection of Gaussian signals via hexagonal sensor networks

2009/02/14 by Paolo Frasca, Paolo Mason, Frasca, Paolo +3
Computer Science · #93A14 (Primary) 94C15 68M14 #Distributed Control Multi-Agent Systems #Energy Efficient Wireless Sensor Networks #FOS: Electrical engineering #FOS: Mathematics #Optimization and Control (math.OC) #Systems and Control (eess.SY) #Target Tracking and Data Fusion in Sensor Networks #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.0902.2446

openalex publication_date 2009/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper considers a special case of the problem of identifying a static scalar signal, depending on the location, using a planar network of sensors in a distributed fashion. Motivated by the application to monitoring wild-fires spreading and pollutants dispersion, we assume the signal to be Gaussian in space. Using a network of sensors positioned to form a regular hexagonal tessellation, we prove that each node can estimate the parameters of the Gaussian from local measurements. Moreover, we study the sensitivity of these estimates to additive errors affecting the measurements. Finally, we show how a consensus algorithm can be designed to fuse the local estimates into a shared global estimate, effectively compensating the measurement errors.

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