Space-time percolation and detection by mobile nodes
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Consider a Poisson point process of intensity \lambda in R^d. We denote
the points as nodes and let each node move as an independent Brownian
motion. Consider a target particle that is initially placed at the origin
at time 0 and can move according to any continuous function. We say that
the target is detected at time t if there exists at least one node within
distance 1 of the target at time t. We show that if \lambda is
sufficiently large, the target will eventually be detected even if
its motion can depend on the past, present and future positions of the
nodes. In the proof we use coupling and multi-scale analysis to model
this event as
a fractal percolation process and show that a good event percolates in
space and time.
This talk is part of the Probability series.
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