Radial Basis Function Interpolation for Signal-Model-Independent Localization Articles uri icon

publication date

  • April 2016

start page

  • 2028

end page

  • 2035


  • 7


  • 16

International Standard Serial Number (ISSN)

  • 1530-437X

Electronic International Standard Serial Number (EISSN)

  • 1558-1748


  • In this paper, we propose a novel localization algorithm to be used in applications where the measurement model is neither accurate nor complete. In our algorithm, we apply radial basis function (RBF) interpolation to evaluate the measurement function on the entire surveillance area and, then, estimate the target position. Since the signal function is sparse in the spatial domain, we also propose to use sparse optimization techniques (LASSO) both to efficiently compute the weights for the RBF and to improve the interpolated function quality. Simulation results show good performance in the localization of single and multiple targets.


  • signal model independent localization; interpolation; rbf; lasso regression; wireless sensor networks; regression; accuracy; location; lasso; range