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Clustering Methods for Multi-sensor Data Fusion

Authors: Liu Han; Zhang Lei;

Clustering Methods for Multi-sensor Data Fusion

Abstract

In the network-centric warfare, the complete battlefield situation information and attack information for weapons can be collected and calculated by multiple sensor platforms, and the results can be distributed to the command and control systems and weapon platforms to complete target attack tasks. Track initiation is the primary problem in multi-source multi-target track processing, correct track initiation can not only reduce the calculation burden of post-processing in multi-target tracking, but also be meaningful to the discovery of interfered interesting target. Centralized algorithm first fuses the raw sensor data, and then starts the track tracing with fused points, weight the clustered sample points to fuse sample points. Clustering could be generalized from the active radar side and the passive radar side. Further more, effects caused by obstacle constraints are also discussed.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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