Sparse Bayesian Learning for Decentralized 2D Smoke Source Localization

Kurzfassung

The increasing frequency and severity of forest fires, driven by climate change and human activity, necessitate accurate and efficient modeling of such events. This paper proposes a method leveraging prior knowledge in the form of a stationary, inhomogeneous advection-diffusion Partial Differential Equation (PDE), combined with decentralized, over-the-network sparse Bayesian learning (SBL), to enable a multi-agent system to localize smoke sources and reconstruct spatial smoke concentration based on collected smoke concentration samples. The approach employs Green's functions for solving the PDE and nonlinear optimization for grid-less estimation of sources based on cooperatively acquired data. A novel iterative initialization strategy is introduced to enhance convergence and promote sparsity in source localization. Simulations validate the method’s effectiveness.