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Motivated by the problem of distributed signal processing in sensor networks, the paper considers the general problem of state estimation in geographically dispersed systems with nonlinear dynamics operating in an uncertain environment with communication constraints. Distributed particle filter implementations used as nonlinear state estimators introduce an additional consensus step, which must converge...
We propose an iterative extension of the covariance intersection (CI) algorithm for distributed data fusion. Our iterative CI (ICI) algorithm is able to disseminate local information throughout the network. We show that the ICI algorithm converges asymptotically to a consensus across all network nodes. We furthermore apply the ICI algorithm to distributed sequential Bayesian estimation and propose...
In this paper we consider a network of agents monitoring a spatially distributed traffic process. Each node measures the number of arrivals seen at its monitoring point in a given time-interval. We propose an asynchronous distributed approach based on a hierarchical Bayes model with unknown hyperparameter, which allows each node to compute the minimum mean square error (MMSE) estimator of the local...
In this paper, we present a distributed algorithm to remove eye blink artifacts from electroencephalography (EEG) signals recorded in a modular high-density EEG system, referred to as a wireless EEG sensor network (WESN). A WESN is a particular instance of a wireless body area network for long-term non-invasive neuromonitoring, which is amenable to extreme miniaturization and low-power system design...
We show how the convergence time of an adaptive network can be estimated in a distributed manner by the agents. Using this procedure, we propose a distributed mechanism for the nodes to switch from using fixed doubly-stochastic combination weights to adaptive combination weights. By doing so, and by knowing when to switch, the agents are able to enhance their steady-state mean-square-error performance...
Wideband spectrum sensing improves the agility of spectrum sensing and spectrum hand-off in cognitive radio systems. In this paper, a distributed wideband spectrum sensing technique over adaptive diffusion networks is proposed. Considering unknown and different channels between the primary and the cognitive users, an averaged received power spectrum across all the cognitive users is estimated by each...
We envisage a wireless sensor network (WSN) where each node is tasked with estimating a set of node-specific desired signals that has been corrupted by additive noise. The nodes accomplish this estimation by means of the distributed adaptive node-specific estimation (DANSE) algorithm in a tree topology (T-DANSE). In this paper, we consider a network where there is at least one node with a large (virtually...
We consider Total Least Squares (TLS) estimation in a network in which each node has access to a subset of equations of an overdetermined linear system. Previous distributed approaches require that the number of equations at each node be larger than the dimension L of the unknown parameter. We present novel distributed TLS estimators which can handle as few as a single equation per node. In the first...
The paper develops a fusion-based, reduced order, distributed implementation of the unscented particle filter (FR/DUPF) for state estimation in complex nonlinear electric power grids (EPG). Based on partitioning the overall EPG system into nsub localized but dynamically coupled subsystems, the near-optimal FR/DUPF provides a computational saving of up to a factor of nsub over the centralized particle...
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