Overlapping Covariance Intersection: Fusion with Partial Structural Knowledge of Correlation from Multiple Sources
This addresses fusion challenges in large-scale engineering systems like situational awareness, but it is incremental as it extends existing covariance intersection methods.
The paper tackles the problem of distributed fusion in large-scale systems where tracking cross-correlations is infeasible, by introducing Overlapping Covariance Intersection (OCI) to incorporate structural knowledge of correlation from multiple sources, resulting in improved fusion performance with efficient computation via semidefinite programming.
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.