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A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks

Abstrakt

This article proposes the use of a feedforward neural network (FNN) to select the starting point for the first iteration in well-known iterative location estimation algorithms, with the research objective of finding the minimum size of a neural network that allows iterative position estimation algorithms to converge in an example positioning network. The selected algorithms for iterative position estimation, the structure of the neural network and how the FNN is used in 2D and 3D position estimation process are presented. The most important results of the work are the parameters of various FNN network structures that resulted in a 100% probability of convergence of iterative position estimation algorithms in the exemplary TDoA positioning network, as well as the average and maximum number of iterations, which can give a general idea about the effectiveness of using neural networks to support the position estimation process. In all simulated scenarios, simple networks with a single hidden layer containing a dozen non-linear neurons turned out to be sufficient to solve the convergence problem.

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
SENSORS nr 24,
ISSN: 1424-8220
Język:
angielski
Rok wydania:
2024
Opis bibliograficzny:
Sadowski J., Stefański J.: A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks// SENSORS -,iss. 2 (2024), s.332-
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/s24020332
Źródła finansowania:
  • Publikacja bezkosztowa
Weryfikacja:
Politechnika Gdańska

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