IEEE Open Journal of Signal Processing (Jan 2022)

When the Closest Targets Make the Difference: An Analysis of Data Association Errors

  • Stefano Marano,
  • Paolo Braca,
  • Leonardo Maria Millefiori,
  • Peter Willett,
  • W. Dale Blair

DOI
https://doi.org/10.1109/OJSP.2022.3207106
Journal volume & issue
Vol. 3
pp. 372 – 386

Abstract

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Multi-object data fusion – combining measurements or estimates of more than one truth object from more than one observer – requires a first “data association” step of deciding which data have common truth objects. Here we focus on the case of two observers only, with the data association engine powered by a polynomially-complex list-matching algorithm such as of Jonker-Volgenant-Castanon (JVC), auction or Munkres. The paper's purpose is to develop an approximation for the probability of assignment error: How often does the data association engine tell the fuser to combine data from truth objects that do not go together? We assume data with Gaussian errors and a Poisson field of truth objects, and we focus on the low-noise case where errors are infrequent and fusion makes sense. In this article, for isotropic, independent identically distributed errors, a single scalar parameter representative of the scene complexity is identified and, exploiting that, a reasonably simple approximate expression for the association error is derived.

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