Distributed Superresolution Gas Source Localization Based on Poisson Equation

Abstract

Accurate modeling and estimation of airborne material in Chemical, Biological, Radiological, or Nuclear acci-dents are vital for effective disaster response. In this paper a method that combines prior domain knowledge in terms of Partial Differential Equations (PDEs), sparse Bayesian learning (SBL), and cooperative estimation for multiple robots or sensor networks is proposed to identify the number and locations of gas sources. Using method of Green's functions and the adjoint state method, a gradient-based optimization with respect to source location is derived, allowing superresolving (arbitrary) source locations. By combing the latter with SBL, a sparse source support can be identified, thus indirectly assessing the number of sources. Both steps are computed cooperatively, utilizing the agent network to share information. Simulation results demonstrate the effectiveness of the approach.