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Updated: Feb 7, 2026

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Noise enhances odor source localization.

Francesco Marcolli1, Martin James1, Agnese Seminara1

  • 1Machine Learning Genoa Center (MaLGa) & Department of Civil, Chemical and Environmental Engineering, University of Genoa, Genoa, Italy.

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|February 6, 2026
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Summary
This summary is machine-generated.

Proprioceptive noise, or uncertainty in sensor location, surprisingly enhances odor plume tracking accuracy in turbulent environments. Optimal noise levels can improve Bayesian inference by leveraging plume geometry and breaking correlations.

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Area of Science:

  • Fluid dynamics
  • Sensory neuroscience
  • Robotics

Background:

  • Odor plume tracking is crucial for biological and robotic systems.
  • Turbulence complicates accurate odor source localization.
  • Biological systems exhibit distributed chemosensation.

Purpose of the Study:

  • Investigate the impact of proprioceptive noise on Bayesian inference for odor source localization.
  • Determine if noise degrades or enhances localization accuracy in turbulent flows.

Main Methods:

  • Simulated odor plume dispersion in a turbulent environment.
  • Incorporated proprioceptive noise (sensor location uncertainty) into Bayesian inference models.
  • Analyzed the effect of varying noise levels on localization accuracy.

Main Results:

  • Proprioceptive noise unexpectedly improved Bayesian inference accuracy under net fluid flow.
  • An optimal level of noise was identified that leverages odor plume geometry.
  • Other noise sources also enhanced accuracy by decorrelating spatiotemporal plume data.

Conclusions:

  • Noise can be beneficial for sensory processing in turbulent environments.
  • Findings suggest potential applications for improving biological and robotic olfaction.
  • Optimizing noise may enhance odor-guided navigation and target detection.