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arXiv · 2310.19000

Distributed Nonlinear Filtering using Triangular Transport Maps

Daniel Grange·Ricardo Baptista·Amirhossein Taghvaei·Allen Tannenbaum·Sean Phillips
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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.

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Daniel Grange, Ricardo Baptista, Amirhossein Taghvaei, Allen Tannenbaum, Sean Phillips. 2023-10-29. Distributed Nonlinear Filtering using Triangular Transport Maps. https://arxiv.org/abs/2310.19000

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