Hyper-arc consistency of polynomial constraints over finite domains using the modified Bernstein form

Bergenti, Federico; Monica, Stefania

doi:10.1007/s10472-017-9544-z

Cited by 2 publications

(1 citation statement)

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“…A related method that is expected to be less expensive in terms of the inherent computational cost can be derived from a recent result [19] that originates from the research on the satisfaction of polynomial constraints over finite domains [30]. Actually, the mentioned result can be used to compute lower and upper bounds for a polynomial function over a box without using Bernstein coefficients.…”

Section: The Post U Algorithmmentioning

confidence: 99%

An Algorithm for Accurate and Robust Indoor Localization Based on Nonlinear Programming

Monica

Bergenti

2020

Electronics

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The study of techniques to estimate the position of mobile devices with a high level of accuracy and robustness is essential to provide advanced location based services in indoor environments. An algorithm to enable mobile devices to estimate their positions in known indoor environments is proposed in this paper under the assumption that fixed anchor nodes are available at known locations. The proposed algorithm is specifically designed to be executed on the mobile device whose position is under investigation, and it allows the device to estimate its position within the environment by actively measuring distance estimates from the anchor nodes. In order to reduce the impact of the errors caused by the arrangement of the anchor nodes in the environment, the proposed algorithm first transforms the localization problem into an optimization problem, and then, it solves the derived optimization problem using techniques inspired by nonlinear programming. Experimental results obtained using ultra-wide band signaling are presented to assess the performance of the algorithm and to compare it with reference alternatives. The presented experimental results confirm that the proposed algorithm provides an increased level of accuracy and robustness with respect to two reference alternatives, regardless of the position of the anchor nodes.

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Section: The Post U Algorithmmentioning

confidence: 99%