A Unified Family-optimal Solution to Covariance Intersection Problems with Semidefinite Programming

TL;DR

This paper introduces a unified semidefinite programming framework for covariance intersection methods, enabling optimal solutions for various CI variants and facilitating real-time distributed estimation.

Contribution

It unifies multiple CI formulations into a single optimization framework, providing family-optimal solutions via semidefinite programming for the first time.

Findings

Unified CI framework with semidefinite programming

Family-optimal solutions for CI and SCI derived

Enables real-time distributed estimation applications

Abstract

Covariance intersection (CI) methods provide a principled approach to fusing estimates with unknown cross-correlations by minimizing a worst-case measure of uncertainty that is consistent with the available information. This paper introduces a generalized CI framework, called overlapping covariance intersection (OCI), which unifies several existing CI formulations within a single optimization-based framework. This unification enables the characterization of family-optimal solutions for multiple CI variants, including standard CI and split covariance intersection (SCI), as solutions to a semidefinite program, for which efficient off-the-shelf solvers are available. When specialized to the corresponding settings, the proposed family-optimal solutions recover the state-of-the-art family-optimal solutions previously reported for CI and SCI. The resulting formulation facilitates the systematic design and real-time implementation of CI-based fusion methods in large-scale distributed estimation problems, such as cooperative localization.