Split Covariance Intersection Filter: Theory and Its Application to Vehicle Localization

Abstract

Data fusion is an important process in a variety of tasks in the intelligent transportation systems field. Most existing data fusion methods rely on the assumption of conditional independence or known statistics of data correlation. In contrast, the split covariance intersection filter (split CIF) was heuristically presented in literature, which aims at providing a mechanism to reasonably handle both known independent information and unknown correlated information in source data. In this paper, we provide a theoretical foundation for the split CIF. First, we clearly specify the consistency definition (coined as split consistency) for estimates in split form. Second, we provide a theoretical proof for the fusion consistency of the split CIF. Finally, we provide a theoretical derivation of the split CIF for the partial observation case. We also present a general architecture of decentralized vehicle localization, which serves as a concrete application example of the split CIF to demonstrate the advantages of the split CIF and how it can potentially benefit vehicle localization (noncooperative and cooperative). In general, this paper aims at providing a baseline for researchers who might intend to incorporate the split CIF (a useful tool for general data fusion) into their prospective research works.