Compartmentalized Covariance Intersection: A Novel Filter Architecture for Distributed Localization

Abstract

This paper introduces the Compartmentalized Covariance Intersection (CCI) algorithm, a consistent technique to fuse measurements in cooperative navigation networks. The algorithm reduces the excess conservatism of standard Covariance Intersection (CI) by assuming that correlation is only present within each measurement stream and not across the different sources. This assumption allows the sources to be compartmentalized and fused with the Kalman equations rather than the CI method, resulting in tighter convergence. The CCI algorithm is applied to a cooperative localization application and is demonstrated to substantially outperform CI, with a covariance that approaches the performance of an ideal centralized estimator in simulations of linear, Gaussian systems. This approach is also demonstrated to be consistent using Monte Carlo simulations. Finally, CCI was used in a laboratory demonstration to perform distributed localization with real-world range measurements between a team of six agents.