Partial consensus and conservative fusion of gaussian mixtures for distributed PHD fusion

Tiancheng Li, Juan M. Corchado, Shudong Sun

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89 引用 (Scopus)

摘要

We propose a novel consensus notion, called 'partial consensus,' for distributed Gaussian mixture probability hypothesis density fusion based on a decentralized sensor network, in which only highly weighted Gaussian components (GCs) are exchanged and fused across neighbor sensors. It is shown that this not only gains high efficiency in both network communication and fusion computation, but also significantly compensates the effects of clutter and missed detections. Two 'conservative' mixture reduction schemes are devised for refining the combined GCs. One is given by pairwise averaging GCs between sensors based on Hungarian assignment and the other merges close GCs for trace minimal, yet, conservative covariance. The close connection of the result to the two approaches, known as covariance union and arithmetic averaging, is unveiled. Simulations based on a sensor network consisting of both linear and nonlinear sensors, have demonstrated the advantage of our approaches over the generalized covariance intersection approach.

源语言英语
文章编号8543158
页(从-至)2150-2163
页数14
期刊IEEE Transactions on Aerospace and Electronic Systems
55
5
DOI
出版状态已出版 - 10月 2019

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