Plume Source Localization on Enceladus by Sequential Monte Carlo Method

Water vapor plumes emanating from the geyser vents at Enceladus's south pole area invite the possibility of direct access to the subsurface liquid reservoir to acquire pristine biological material if it exists. Any descending lander adapted for plume localization is required to not only explore the icy plume environment during its descent, but it must also infer the location of the landing target-the plume source-autonomously. Compared with existing scenarios of terrestrial plume source localization methods, the source likelihood map (SLIM) method for an Enceladus mission offers a more extensive search area, a higher maneuver velocity, and a shorter search time. This paper investigates a particle-based odor source localization (pOSL) approach that offers the prospect of targeting one of the plume sources by autonomously measuring the concentration field. Reasons for the negative likelihood and overfitting issues associated with Bayesian SLIM are analyzed to build a novel probabilistic model. By implementing this model, the proposed pOSL algorithm evaluates the observation likelihood via posterior maximization method and estimates the source location via the sequential Monte Carlo method. The pOSL algorithm resolves difficulties associated with other methods while reducing the time complexity from O(N tau) to O(N). The numerical simulations illustrate that the proposed approach is feasible and permits accurate targeting of Enceladus's geyser vents.