61,437 research outputs found
Convergence Rate Analysis of Distributed Gossip (Linear Parameter) Estimation: Fundamental Limits and Tradeoffs
The paper considers gossip distributed estimation of a (static) distributed
random field (a.k.a., large scale unknown parameter vector) observed by
sparsely interconnected sensors, each of which only observes a small fraction
of the field. We consider linear distributed estimators whose structure
combines the information \emph{flow} among sensors (the \emph{consensus} term
resulting from the local gossiping exchange among sensors when they are able to
communicate) and the information \emph{gathering} measured by the sensors (the
\emph{sensing} or \emph{innovations} term.) This leads to mixed time scale
algorithms--one time scale associated with the consensus and the other with the
innovations. The paper establishes a distributed observability condition
(global observability plus mean connectedness) under which the distributed
estimates are consistent and asymptotically normal. We introduce the
distributed notion equivalent to the (centralized) Fisher information rate,
which is a bound on the mean square error reduction rate of any distributed
estimator; we show that under the appropriate modeling and structural network
communication conditions (gossip protocol) the distributed gossip estimator
attains this distributed Fisher information rate, asymptotically achieving the
performance of the optimal centralized estimator. Finally, we study the
behavior of the distributed gossip estimator when the measurements fade (noise
variance grows) with time; in particular, we consider the maximum rate at which
the noise variance can grow and still the distributed estimator being
consistent, by showing that, as long as the centralized estimator is
consistent, the distributed estimator remains consistent.Comment: Submitted for publication, 30 page
Consensus-based Networked Tracking in Presence of Heterogeneous Time-Delays
We propose a distributed (single) target tracking scheme based on networked
estimation and consensus algorithms over static sensor networks. The tracking
part is based on linear time-difference-of-arrival (TDOA) measurement proposed
in our previous works. This paper, in particular, develops delay-tolerant
distributed filtering solutions over sparse data-transmission networks. We
assume general arbitrary heterogeneous delays at different links. This may
occur in many realistic large-scale applications where the data-sharing between
different nodes is subject to latency due to communication-resource constraints
or large spatially distributed sensor networks. The solution we propose in this
work shows improved performance (verified by both theory and simulations) in
such scenarios. Another privilege of such distributed schemes is the
possibility to add localized fault-detection and isolation (FDI) strategies
along with survivable graph-theoretic design, which opens many follow-up venues
to this research. To our best knowledge no such delay-tolerant distributed
linear algorithm is given in the existing distributed tracking literature.Comment: ICRoM2
Distributed parameter and state estimation for wireless sensor networks
The research in distributed algorithms is linked with the developments of statistical inference
in wireless sensor networks (WSNs) applications. Typically, distributed approaches process
the collected signals from networked sensor nodes. That is to say, the sensors receive local
observations and transmit information between each other. Each sensor is capable of combining
the collected information with its own observations to improve performance. In this thesis, we
propose novel distributed methods for the inference applications using wireless sensor networks.
In particular, the efficient algorithms which are not computationally intensive are investigated.
Moreover, we present a number of novel algorithms for processing asynchronous network events
and robust state estimation.
In the first part of the thesis, a distributed adaptive algorithm based on the component-wise
EM method for decentralized sensor networks is investigated. The distributed component-wise
Expectation-Maximization (EM) algorithm has been designed for application in a Gaussian
density estimation. The proposed algorithm operates a component-wise EM procedure for local
parameter estimation and exploit an incremental strategy for network updating, which can provide
an improved convergence rate. Numerical simulation results have illustrated the advantages of
the proposed distributed component-wise EM algorithm for both well-separated and overlapped
mixture densities. The distributed component-wise EM algorithm can outperform other EM-based
distributed algorithms in estimating overlapping Gaussian mixtures.
In the second part of the thesis, a diffusion based EM gradient algorithm for density estimation
in asynchronous wireless sensor networks has been proposed. Specifically, based on the
asynchronous adapt-then-combine diffusion strategy, a distributed EM gradient algorithm that
can deal with asynchronous network events has been considered. The Bernoulli model has been
exploited to approximate the asynchronous behaviour of the network. Compared with existing
distributed EM based estimation methods using a consensus strategy, the proposed algorithm
can provide more accurate estimates in the presence of asynchronous networks uncertainties,
such as random link failures, random data arrival times, and turning on or off sensor nodes
for energy conservation. Simulation experiments have been demonstrated that the proposed
algorithm significantly outperforms the consensus based strategies in terms of Mean-Square-
Deviation (MSD) performance in an asynchronous network setting.
Finally, the challenge of distributed state estimation in power systems which requires low
complexity and high stability in the presence of bad data for a large scale network is addressed.
A gossip based quasi-Newton algorithm has been proposed for solving the power system state
estimation problem. In particular, we have applied the quasi-Newton method for distributed
state estimation under the gossip protocol. The proposed algorithm exploits the Broyden-
Fletcher-Goldfarb-Shanno (BFGS) formula to approximate the Hessian matrix, thus avoiding the
computation of inverse Hessian matrices for each control area. The simulation results for IEEE
14 bus system and a large scale 4200 bus system have shown that the distributed quasi-Newton
scheme outperforms existing algorithms in terms of Mean-Square-Error (MSE) performance with
bad data
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
Distributed Parameter Estimation via Pseudo-likelihood
Estimating statistical models within sensor networks requires distributed
algorithms, in which both data and computation are distributed across the nodes
of the network. We propose a general approach for distributed learning based on
combining local estimators defined by pseudo-likelihood components,
encompassing a number of combination methods, and provide both theoretical and
experimental analysis. We show that simple linear combination or max-voting
methods, when combined with second-order information, are statistically
competitive with more advanced and costly joint optimization. Our algorithms
have many attractive properties including low communication and computational
cost and "any-time" behavior.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
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
Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics
This paper focuses on the problem of recursive nonlinear least squares
parameter estimation in multi-agent networks, in which the individual agents
observe sequentially over time an independent and identically distributed
(i.i.d.) time-series consisting of a nonlinear function of the true but unknown
parameter corrupted by noise. A distributed recursive estimator of the
\emph{consensus} + \emph{innovations} type, namely , is
proposed, in which the agents update their parameter estimates at each
observation sampling epoch in a collaborative way by simultaneously processing
the latest locally sensed information~(\emph{innovations}) and the parameter
estimates from other agents~(\emph{consensus}) in the local neighborhood
conforming to a pre-specified inter-agent communication topology. Under rather
weak conditions on the connectivity of the inter-agent communication and a
\emph{global observability} criterion, it is shown that at every network agent,
the proposed algorithm leads to consistent parameter estimates. Furthermore,
under standard smoothness assumptions on the local observation functions, the
distributed estimator is shown to yield order-optimal convergence rates, i.e.,
as far as the order of pathwise convergence is concerned, the local parameter
estimates at each agent are as good as the optimal centralized nonlinear least
squares estimator which would require access to all the observations across all
the agents at all times. In order to benchmark the performance of the proposed
distributed estimator with that of the centralized nonlinear
least squares estimator, the asymptotic normality of the estimate sequence is
established and the asymptotic covariance of the distributed estimator is
evaluated. Finally, simulation results are presented which illustrate and
verify the analytical findings.Comment: 28 pages. Initial Submission: Feb. 2016, Revised: July 2016,
Accepted: September 2016, To appear in IEEE Transactions on Signal and
Information Processing over Networks: Special Issue on Inference and Learning
over Network
- …