2,203 research outputs found
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
Efficient delay-tolerant particle filtering
This 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. The framework requires the
identification of a threshold that separates informative from uninformative;
this threshold selection task is formulated as a constrained optimization
problem, where the goal is to minimize tracking error whilst controlling the
computational requirements. We develop an algorithm that provides an
approximate solution for the optimization problem. Simulation experiments
provide an example where the proposed framework processes less than 40% of all
OOSMs with only a small reduction in tracking accuracy
Controlled diffusion processes
This article gives an overview of the developments in controlled diffusion
processes, emphasizing key results regarding existence of optimal controls and
their characterization via dynamic programming for a variety of cost criteria
and structural assumptions. Stochastic maximum principle and control under
partial observations (equivalently, control of nonlinear filters) are also
discussed. Several other related topics are briefly sketched.Comment: Published at http://dx.doi.org/10.1214/154957805100000131 in the
Probability Surveys (http://www.i-journals.org/ps/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Online Learning of Dynamic Parameters in Social Networks
This paper addresses the problem of online learning in a dynamic setting. We
consider a social network in which each individual observes a private signal
about the underlying state of the world and communicates with her neighbors at
each time period. Unlike many existing approaches, the underlying state is
dynamic, and evolves according to a geometric random walk. We view the scenario
as an optimization problem where agents aim to learn the true state while
suffering the smallest possible loss. Based on the decomposition of the global
loss function, we introduce two update mechanisms, each of which generates an
estimate of the true state. We establish a tight bound on the rate of change of
the underlying state, under which individuals can track the parameter with a
bounded variance. Then, we characterize explicit expressions for the steady
state mean-square deviation(MSD) of the estimates from the truth, per
individual. We observe that only one of the estimators recovers the optimal
MSD, which underscores the impact of the objective function decomposition on
the learning quality. Finally, we provide an upper bound on the regret of the
proposed methods, measured as an average of errors in estimating the parameter
in a finite time.Comment: 12 pages, To appear in Neural Information Processing Systems (NIPS)
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