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Laureano, M.A.P. and Tonidandel, F. (2019) Analysis of the PSO Parameters for a Robots Positioning System in SSL. In: Chalup, S., Niemueller, T., Suthakorn, J., Williams, MA., Eds., Robot World Cup, Springer, Cham, 126-139.
https://doi.org/10.1007/978-3-030-35699-6_10

has been cited by the following article:

• TITLE: An Improved Genetic Algorithm for UWB Localization

  AUTHORS: Xianzhi Zheng

  KEYWORDS: Location, Improved Genetic Algorithm, Localization Accuracy, UWB

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.10, October 14, 2022

  ABSTRACT: The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information between tags, resulting in insufficient ranging information and limited improvement of the localization accuracy. In view of this, an improved genetic localization algorithm is proposed. First, a new fitness function is constructed, which not only includes the ranging information between the tag and the base station, but also the ranging information between the tags to ensure that the ranging information is fully utilized in the localization process. Then, the search method based on Brownian motion is adopted to ensure that the improved algorithm can speed up the convergence speed of the localization result. The simulation results show that, compared with the traditional genetic localization algorithm, the improved genetic localization algorithm can reduce the influence of the ranging error on the localization error and improve the localization performance.