Cooperative Decentralised Data Fusion Using Probability Collectives
نویسندگان
چکیده
This paper demonstrates how Probability Collectives (PC), a powerful new framework for distributed optimisation, can be used to coordinate sensing actions in a decentralised sensor network. In particular, a sensor-to-target assignment problem is investigated. Decentralised data fusion (DDF) is an efficient way to share and fuse information about target positions over a sensor network. PC is an efficient way to sample the joint space of sensor actions to discover an optimal sensing strategy. Both frameworks are underpinned by a probabilistic information update. In this paper, the performance of a PC algorithm for a generic sensor management problem is baselined against “random” and “selfish” strategies. PC outperforms the baselines and moreover is found to achieve the global (centralised) optimum. Further experiments indicate how PC’s intrinsic sampling step might be modified to reduce its impact on communication loading across the network.
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تاریخ انتشار 2007