期刊
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
卷 55, 期 6, 页码 2759-2773出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAES.2019.2893083
关键词
Uncertainty; Signal processing algorithms; Probability density function; Sensors; Message passing; Licenses; Covariance intersection (CI); exponential mixture density (EMD); multisensor fusion; random finite sets (RFS); target tracking
资金
- Engineering and Physical Sciences Research Council [EP/K014277/1]
- MOD University Defence Research Collaboration in Signal Processing
- EPSRC [EP/K014277/1] Funding Source: UKRI
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.
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