Distributed Nonlinear Filtering using Triangular Transport Maps

Abstract

The distributed filtering problem sequentially estimates a global state variable using observations from a network of local sensors with different measurement models. In this work, we introduce a novel methodology for distributed nonlinear filtering by combining techniques from transportation of measures, dimensionality reduction, and consensus algorithms. We illustrate our methodology on a satellite pose estimation problem from a network of direct and indirect observers. The numerical results serve as a proof of concept, offering new venues for theoretical and applied research in the domain of distributed filtering.