42,800 research outputs found
On the Limited Communication Analysis and Design for Decentralized Estimation
This paper pertains to the analysis and design of decentralized estimation
schemes that make use of limited communication. Briefly, these schemes equip
the sensors with scalar states that iteratively merge the measurements and the
state of other sensors to be used for state estimation. Contrarily to commonly
used distributed estimation schemes, the only information being exchanged are
scalars, there is only one common time-scale for communication and estimation,
and the retrieval of the state of the system and sensors is achieved in
finite-time. We extend previous work to a more general setup and provide
necessary and sufficient conditions required for the communication between the
sensors that enable the use of limited communication decentralized
estimation~schemes. Additionally, we discuss the cases where the sensors are
memoryless, and where the sensors might not have the capacity to discern the
contributions of other sensors. Based on these conditions and the fact that
communication channels incur a cost, we cast the problem of finding the minimum
cost communication graph that enables limited communication decentralized
estimation schemes as an integer programming problem.Comment: Updates on the paper in CDC 201
A decentralized motion coordination strategy for dynamic target tracking
This paper presents a decentralized motion planning
algorithm for the distributed sensing of a noisy dynamical
process by multiple cooperating mobile sensor agents. This
problem is motivated by localization and tracking tasks of
dynamic targets. Our gradient-descent method is based on a
cost function that measures the overall quality of sensing. We
also investigate the role of imperfect communication between
sensor agents in this framework, and examine the trade-offs in
performance between sensing and communication. Simulations
illustrate the basic characteristics of the algorithms
Cooperative H-infinity Estimation for Large-Scale Interconnected Linear Systems
In this paper, a synthesis method for distributed estimation is presented,
which is suitable for dealing with large-scale interconnected linear systems
with disturbance. The main feature of the proposed method is that local
estimators only estimate a reduced set of state variables and their complexity
does not increase with the size of the system. Nevertheless, the local
estimators are able to deal with lack of local detectability. Moreover, the
estimators guarantee H-infinity-performance of the estimates with respect to
model and measurement disturbances.Comment: Short version published in Proc. American Control Conference (ACC),
pp.2119-2124. Chicago, IL, 201
Monte Carlo optimization of decentralized estimation networks over directed acyclic graphs under communication constraints
Motivated by the vision of sensor networks, we consider decentralized estimation networks over bandwidthâlimited communication links, and are particularly interested in the tradeoff between the estimation accuracy and the cost of communications due to, e.g., energy consumption. We employ a class of inânetwork processing strategies that admits directed acyclic graph representations and yields a tractable Bayesian risk that comprises the cost of communications and estimation error penalty. This perspective captures a broad range of possibilities for processing under network constraints and enables a rigorous design problem in the form of constrained optimization. A similar scheme and the structures exhibited by the solutions have been previously studied in the context of decentralized detection. Under reasonable assumptions, the optimization can be carried out in a message passing fashion. We adopt
this framework for estimation, however, the corresponding optimization scheme involves integral operators that cannot be evaluated exactly in general. We develop an approximation framework using Monte Carlo methods and obtain
particle representations and approximate computational schemes for both the inânetwork processing strategies and their optimization. The proposed Monte Carlo optimization procedure operates in a scalable and efficient fashion and,
owing to the non-parametric nature, can produce results for any distributions provided that samples can be produced from the marginals. In addition, this approach exhibits graceful degradation of the estimation accuracy asymptotically
as the communication becomes more costly, through a parameterized Bayesian risk
Monte Carlo optimization approach for decentralized estimation networks under communication constraints
We consider designing decentralized estimation schemes over bandwidth limited communication links with a particular interest in the tradeoff between the estimation accuracy and the cost of communications due to, e.g., energy
consumption. We take two classes of inânetwork processing strategies into account which yield graph representations through modeling the sensor platforms as the vertices and the communication links by edges as well as a tractable
Bayesian risk that comprises the cost of transmissions and penalty for the estimation errors. This approach captures a broad range of possibilities for âonlineâ processing of observations as well as the constraints imposed and enables a rigorous design setting in the form of a constrained optimization problem. Similar schemes as well as the structures exhibited by the solutions to the design problem has been studied previously in the context of decentralized detection. Under reasonable assumptions, the optimization can be carried out in a message passing fashion. We adopt this framework for estimation, however, the corresponding optimization schemes involve integral operators that cannot
be evaluated exactly in general. We develop an approximation framework using Monte Carlo methods and obtain particle representations and approximate computational schemes for both classes of inânetwork processing strategies
and their optimization. The proposed Monte Carlo optimization procedures operate in a scalable and efficient fashion and, owing to the non-parametric nature, can produce results for any distributions provided that samples can be
produced from the marginals. In addition, this approach exhibits graceful degradation of the estimation accuracy asymptotically as the communication becomes more costly, through a parameterized Bayesian risk
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