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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Fusion of Finite-Set Distributions: Pointwise Consistency and Global Cardinality
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Fusion of Finite-Set Distributions: Pointwise Consistency and Global Cardinality

机译：有限集分布的融合：点一致性和全局基数

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摘要

A recent trend in distributed multisensor fusion is to use random finite-set filters at the sensor nodes and fuse the filtered distributions algorithmically using their exponential mixture densities (EMDs). Fusion algorithms that extend covariance intersection and consensus-based approaches are such examples. In this paper, we analyze the variational principle underlying EMDs and show that the EMDs of finite-set distributions do not necessarily lead to consistent fusion of cardinality distributions. Indeed, we demonstrate that these inconsistencies may occur with overwhelming probability in practice, through examples with Bernoulli, Poisson, and independent identically distributed cluster processes. We prove that pointwise consistency of EMDs does not imply consistency in global cardinality and vice versa. Then, we redefine the variational problems underlying fusion and provide iterative solutions thereby establishing a framework that guarantees cardinality consistent fusion.
机译：最近分布式多传感器融合的趋势是在传感器节点上使用随机有限集滤波器，并使用其指数混合密度（EMD）融合算法算法的滤波分布。扩展协方差交叉路口和基于共识的方法的融合算法是这样的例子。在本文中，我们分析了基础的变分原理，并表明有限设定分布的EMD不一定导致基数分布一致融合。事实上，我们证明这些不一致性可能在实践中的压倒性概率，通过伯努利，泊松和独立相同的分布式集群进程。我们证明了EMD的尖端并不意味着全球基数的一致性，反之亦然。然后，我们重新定义了融合潜在的变分问题，并提供了迭代解决方案，从而建立了保证基数一致融合的框架。

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