Overlapping Covariance Intersection: Fusion with Partial Structural Knowledge of Correlation from Multiple Sources

TL;DR

This paper introduces Overlapping Covariance Intersection (OCI), a new fusion method that incorporates partial structural correlation knowledge in distributed systems, improving robustness and efficiency in large-scale sensor fusion.

Contribution

The paper develops OCI, a generalized covariance intersection framework that leverages structural correlation information, with a formal problem formulation and an efficient semidefinite programming solution.

Findings

OCI improves fusion accuracy in large-scale systems.

The method is computationally efficient for real-time applications.

OCI maintains robustness despite unknown correlations.

Abstract

Emerging large-scale engineering systems rely on distributed fusion for situational awareness, where agents combine noisy local sensor measurements with exchanged information to obtain fused estimates. However, at the sheer scale of these systems, tracking cross-correlations becomes infeasible, preventing the use of optimal filters. Covariance intersection (CI) methods address fusion problems with unknown correlations by minimizing worst-case uncertainty based on available information. Existing CI extensions exploit limited correlation knowledge but cannot incorporate structural knowledge of correlation from multiple sources, which naturally arises in distributed fusion problems. This paper introduces Overlapping Covariance Intersection (OCI), a generalized CI framework that accommodates this novel information structure. We formalize the OCI problem and establish necessary and sufficient conditions for feasibility. We show that a family-optimal solution can be computed efficiently via semidefinite programming, enabling real-time implementation. The proposed tools enable improved fusion performance for large-scale systems while retaining robustness to unknown correlations.