English
资源说明：In the literature, there are claims stating that particle
filters cannot be used for high dimensional systems because
their random measures degenerate to single particles.
While this may be true for standard implementations of particle
filtering, it may not be true for alternative implementations.
In this paper we build on our previous work for tracking
multiple targets with multiple particle filters, where each
particle filter tracks its own target. We avoid the collapse of
traditional particle filtering by considering an interconnected
network of such particle filters where each of them works on a
relatively low dimensional space. We assume that our interest
is in finding the marginal posterior distributions of the state
vectors describing the different targets and not in the joint
posterior of all the targets. We test the method on the problem
of multiple target tracking based on sensor data which
represent a superposition of contributions of all the targets in
the field. The computer simulations demonstrate the performance
of the newly proposed method and compare it with
other implementations of particle filtering.
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