Boris N. Oreshkin
Efficient delay-tolerant particle filtering.
Thu, 09/23/2010 - 15:02 — SomNambulAuthors: Boris N. Oreshkin, Mark J. Coates, Xuan Liu
Subjects: Applications
AbstractThis paper proposes a novel framework for delay-tolerant particle filtering
that is computationally efficient and has limited memory requirements. Within
this framework the informativeness of a delayed (out-of-sequence) measurement
(OOSM) is estimated using a lightweight procedure and uninformative
measurements are immediately discarded.Optimization and Analysis of Distributed Averaging with Short Node Memory.
Mon, 02/08/2010 - 16:02 — SomNambulAuthors: Boris N. Oreshkin, Mark J. Coates, Michael G. Rabbat
Subjects: and Cluster Computing, Distributed, Parallel
AbstractIn this paper, we demonstrate, both theoretically and by numerical examples,
that adding a local prediction component to the update rule can significantly
improve the convergence rate of distributed averaging algorithms. We focus on
the case where the local predictor is a linear combination of the node's two
previous values (i.e., two memory taps), and our update rule computes a
combination of the predictor and the usual weighted linear combination of
values received from neighbouring nodes.- Read more
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Analysis of error propagation in particle filters with approximation.
Fri, 08/21/2009 - 19:51 — SomNambulAuthors: Boris N. Oreshkin, Mark J. Coates
Subjects: Probability
AbstractThis paper examines the impact of approximation steps that become necessary
when particle filters are implemented on resource-constrained platforms. We
consider particle filters that perform intermittent approximation, either by
subsampling the particles or by generating a parametric approximation. For such
algorithms, we derive time-uniform bounds on the weak-sense Lp error and
present associated exponential inequalities.
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