PLOS-RS: Probabilistic Localization of Odor Sources via Random Search

Localizing an odor-emitting source using distributed sensors is a fundamental challenge in autonomous sensing systems, with applications in environmental monitoring, pollution tracking, safety, and molecular communication. However, in practical implementations, centralized inference over high-resolution grids is computationally expensive and the complex dynamics of molecular dispersion pose significant challenges for reliable and efficient odor source localization (OSL). We propose a network-aware, resource-efficient solution for localizing the source in macro-scale environments where modeling of molecular concentration is governed by the advection-diffusion equation. Instead of computing the posterior/likelihood over the entire grid, we implement lightweight maximum a posteriori (MAP)/maximum likelihood estimation (MLE) methods using randomized candidate sampling. Each candidate location is evaluated through a PDE-based forward model guided by Gaussian prior centered on current estimate, and the best estimate guides the next round of sampling. This yields a convergent and cost-efficient localization strategy. We evaluate our method using synthetic experiments across various conditions. Results show convergence to the true source with decreasing estimation variance. We further evaluate estimator efficiency via the Cramér-Rao Lower Bound (CRLB) for this setting using finite-difference approximations and demonstrate that our method closely approaches this bound. Our open-source, modular simulation framework enables reproducible experiments and further research on OSL.