基于指数混合模型的次优分布式数据融合

2006 IEEE Nonlinear Statistical Signal Processing Workshop Pub Date : 2006-09-01 DOI:10.1109/NSSPW.2006.4378844

S. Julier, T. Bailey, J. Uhlmann

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引用次数: 72

摘要

本文研究了指数混合密度(EMDs)作为分布式数据融合的次优更新规则。我们证明了emd在概率分布的最小值上有一个“从下”的逐点边界。然而，这些分布并不是有界的，因此可以解释为一个融合操作。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Using Exponential Mixture Models for Suboptimal Distributed Data Fusion

In this paper we investigate the use of Exponential Mixture Densities (EMDs) as suboptimal update rules for distributed data fusion. We show that EMDs have a pointwise bound "from below" on the minimum value of the probability distribution. However, the distributions are not bounded from above and thus can be interpreted as a fusion operation.

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2006 IEEE Nonlinear Statistical Signal Processing Workshop

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