Split Covariance Intersection Filter: Theory and Its Application to Vehicle Localization
Split Covariance Intersection Filter: Theory and Its Application to Vehicle Localization
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DOI：
10.1109/tits.2013.2267800
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发表时间：
2013-12-01
影响因子：
8.5
通讯作者：
Yang, Ming
中科院分区：
文献类型：
--
作者：
Li, Hao;Nashashibi, Fawzi;Yang, Ming
数据融合是智能交通系统中各种任务的一个重要过程。现有的数据融合方法大多依赖于条件独立性假设或数据相关性的已知统计量。而分裂协方差交叉滤波器（split covariance intersection filter，split CIF）则是在已有文献中提出的一种新的滤波方法，其目的是提供一种既能合理处理源数据中已知独立信息又能合理处理源数据中未知相关信息的机制。本文为分离式CIF提供了理论基础。首先，我们清楚地指定了分裂形式估计的一致性定义（称为分裂一致性）。其次，从理论上证明了分裂CIF的融合一致性。最后，我们提供了一个理论推导的分裂CIF的部分观测情况。我们还提出了一个分散式车辆定位的一般架构，作为一个具体的应用实例的分裂CIF证明分裂CIF的优点，以及它如何可以潜在地有利于车辆定位（非合作和合作）。总的来说，本文的目的是提供一个基线的研究人员谁可能打算将分裂CIF（一个有用的工具，一般的数据融合）到他们的未来的研究工作。
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.