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March 25, 2026Open Access

Optimal Transport as a Reduction Technique for Deterministic Nonlinear Filtering

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Authors

FGFelipe Giraldo-GruesoAPAndrey A. PopovUHUwe D. Hanebeck

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Overview

This work demonstrates a new deterministic sampling technique for state estimation, indicating improved computational efficiency.

Key Points

  • To develop a computationally efficient sampling strategy for state estimation using optimal transport methods.
  • Introduced a new deterministic sampling strategy that is computationally inexpensive.
  • Utilized the iterative Sinkhorn-Knopp algorithm for solving the approximate optimal transport problem.
  • Compared the new method to the existing modified Cramér-von Mises distance approach.
  • The new technique effectively samples from Gaussian mixtures with reduced computational cost.
  • Demonstrated improved feasibility for onboard applications compared to previous methods.

Cite This Study

Giraldo-Grueso et al. (2025) studied this question.

synapsesocial.com/papers/69c37b54b34aaaeb1a67d9bahttps://doi.org/10.5445/ir/1000186779
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