451 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
Distributed estimation over a low-cost sensor network: a review of state-of-the-art
Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted
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
On the genericity properties in networked estimation: Topology design and sensor placement
In this paper, we consider networked estimation of linear, discrete-time
dynamical systems monitored by a network of agents. In order to minimize the
power requirement at the (possibly, battery-operated) agents, we require that
the agents can exchange information with their neighbors only \emph{once per
dynamical system time-step}; in contrast to consensus-based estimation where
the agents exchange information until they reach a consensus. It can be
verified that with this restriction on information exchange, measurement fusion
alone results in an unbounded estimation error at every such agent that does
not have an observable set of measurements in its neighborhood. To over come
this challenge, state-estimate fusion has been proposed to recover the system
observability. However, we show that adding state-estimate fusion may not
recover observability when the system matrix is structured-rank (-rank)
deficient.
In this context, we characterize the state-estimate fusion and measurement
fusion under both full -rank and -rank deficient system matrices.Comment: submitted for IEEE journal publicatio
Compressive Diffusion Strategies Over Distributed Networks for Reduced Communication Load
We study the compressive diffusion strategies over distributed networks based
on the diffusion implementation and adaptive extraction of the information from
the compressed diffusion data. We demonstrate that one can achieve a comparable
performance with the full information exchange configurations, even if the
diffused information is compressed into a scalar or a single bit. To this end,
we provide a complete performance analysis for the compressive diffusion
strategies. We analyze the transient, steady-state and tracking performance of
the configurations in which the diffused data is compressed into a scalar or a
single-bit. We propose a new adaptive combination method improving the
convergence performance of the compressive diffusion strategies further. In the
new method, we introduce one more freedom-of-dimension in the combination
matrix and adapt it by using the conventional mixture approach in order to
enhance the convergence performance for any possible combination rule used for
the full diffusion configuration. We demonstrate that our theoretical analysis
closely follow the ensemble averaged results in our simulations. We provide
numerical examples showing the improved convergence performance with the new
adaptive combination method.Comment: Submitted to IEEE Transactions on Signal Processin
Networked Signal and Information Processing
The article reviews significant advances in networked signal and information
processing, which have enabled in the last 25 years extending decision making
and inference, optimization, control, and learning to the increasingly
ubiquitous environments of distributed agents. As these interacting agents
cooperate, new collective behaviors emerge from local decisions and actions.
Moreover, and significantly, theory and applications show that networked
agents, through cooperation and sharing, are able to match the performance of
cloud or federated solutions, while offering the potential for improved
privacy, increasing resilience, and saving resources
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