A Sheaf Theoretical Approach to Uncertainty Quantification of Heterogeneous Geolocation Information

TL;DR

This paper introduces a sheaf theoretical framework for integrating heterogeneous sensor data in multi-target tracking, providing a unified, interpretable, and robust method for uncertainty quantification and sensor data reconciliation.

Contribution

It applies sheaf theory to sensor data integration, offering algorithms for data globalization and diagnostics, and demonstrates its effectiveness through real-world wildlife tracking experiments.

Findings

Sheaf-based models successfully integrated heterogeneous sensor data.

The sheaf approach recovered accurate target locations and behaviors.

It remained valid even with unnormalized, complex sensor observations.

Abstract

Integration of heterogeneous sensors is a challenging problem across a range of applications. Prominent among these are multi-target tracking, where one must combine observations from different sensor types in a meaningful way to track multiple targets. Because sensors have differing error models, we seek a theoretically-justified quantification of the agreement among ensembles of sensors, both overall for a sensor collection, and also at a fine-grained level specifying pairwise and multi-way interactions among sensors. We demonstrate that the theory of mathematical sheaves provides a unified answer to this need, supporting both quantitative and qualitative data. The theory provides algorithms to globalize data across the network of deployed sensors, and to diagnose issues when the data do not globalize cleanly. We demonstrate the utility of sheaf-based tracking models based on experimental data of a wild population of black bears in Asheville, North Carolina. A measurement model involving four sensors deployed among the bears and the team of scientists charged with tracking their location is deployed. This provides a sheaf-based integration model which is small enough to fully interpret, but of sufficient complexity to demonstrate the sheaf's ability to recover a holistic picture of the locations and behaviors of both individual bears and the bear-human tracking system. A statistical approach was developed for comparison, a dynamic linear model which was estimated using a Kalman filter. This approach also recovered bear and human locations and sensor accuracies. When the observations are normalized into a common coordinate system, the structure of the dynamic linear observation model recapitulates the structure of the sheaf model, demonstrating the canonicity of the sheaf-based approach. But when the observations are not so normalized, the sheaf model still remains valid.