Decentralized data fusion with inverse covariance intersection
Decentralized data fusion with inverse covariance intersection
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DOI：
10.1016/j.automatica.2017.01.019
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发表时间：
2017-05-01
期刊：
影响因子：
6.4
通讯作者：
Hanebeck, Uwe D.
中科院分区：
文献类型：
--
作者：
Noack, Benjamin;Sijs, Joris;Hanebeck, Uwe D.
在分布式和分散式状态估计系统中，融合方法被用来系统地将多个状态估计合并为一个更准确的估计。融合过程中经常遇到的一个问题涉及未知的公共信息，这些信息由待融合的估计共享，并负责相关性。如果融合方法不知道相关结构，则通常采用保守策略。因此，椭球交集方法引入的参数化为描述未知相关性的一种新方法，尽管这些参数的合适值还没有被证明是一致的。本文提出了一种扩展的椭球交集，在未知公共信息存在的情况下，保证了融合结果的一致性。该方法所用的界对应于计算逆协方差椭球交点上的外椭球界。作为这种逆协方差交集方法的一大优势，融合结果被证明比著名的协方差交集方法提供的结果更准确。(C)2017爱思唯尔有限公司。保留所有权利。
In distributed and decentralized state estimation systems, fusion methods are employed to systematically combine multiple estimates of the state into a single, more accurate estimate. An often encountered problem in the fusion process relates to unknown common information that is shared by the estimates to be fused and is responsible for correlations. If the correlation structure is unknown to the fusion method, conservative strategies are typically pursued. As such, the parameterization introduced by the ellipsoidal intersection method has been a novel approach to describe unknown correlations, though suitable values for these parameters with proven consistency have not been identified yet. In this article, an extension of ellipsoidal intersection is proposed that guarantees consistent fusion results in the presence of unknown common information. The bound used by the novel approach corresponds to computing an outer ellipsoidal bound on the intersection of inverse covariance ellipsoids. As a major advantage of this inverse covariance intersection method, fusion results prove to be more accurate than those provided by the well-known covariance intersection method. (C) 2017 Elsevier Ltd. All rights reserved.