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Adaptive sampling of parametric fields

Adaptive sampling of parametric fields

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This chapter discusses background work related to the deployment of mobile robots for sampling. Section 3.1 presents different sampling strategies. Section 3.2 describes the density estimation by sampling and sensor fusion. Sections 3.3 and 3.4 explain existing approaches for sampling using static and mobile sensor nodes, respectively. Parametric and non-parametric solutions to the sampling problem are also discussed. Section 3.5 presents the existing approaches for reducing the localization error in mobile robots while sampling. Following this, the chapter focuses on the mathematical formulation of adaptive sampling (AS) problem for parameterized fields, including models, uncertainties and sampling criteria. Section 3.6 discusses sampling strategies such as raster scanning, random sampling, AS and greedy AS (GAS) and provides both a qualitative and a quantitative definition for the AS problem. Section 3.7 presents the extended Kalman filter (EKF) formulation of the AS problem with a single mobile sensor node.

Inspec keywords: random processes; nonlinear filters; sampling methods; mobile robots; Kalman filters; sensor fusion

Other keywords: sampling strategy; random sampling; adaptive sampling; raster scanning; extended Kalman filter; parameterized field; mobile sensor node; sensor fusion; mathematical formulation; mobile robot; EKF formulation; sampling criteria; AS problem; greedy AS; static sensor node; density estimation; parametric field; localization error

Subjects: Filtering methods in signal processing

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