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Distributed principal subspace estimation in wireless sensor networks
 IEEE J. Sel. Topics Signal Process
, 2011
"... Abstract—Motivated by applications in multisensor array detection and estimation, this paper studies the problem of tracking the principal eigenvector and the principal subspace of a signal’s covariance matrix adaptively in a fully decentralized wireless sensor network (WSN). Sensor networks are tr ..."
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Abstract—Motivated by applications in multisensor array detection and estimation, this paper studies the problem of tracking the principal eigenvector and the principal subspace of a signal’s covariance matrix adaptively in a fully decentralized wireless sensor network (WSN). Sensor networks are traditionally designed to simply gather raw data at a fusion center, where all the processing occurs. In large deployments, this model entails high networking cost and creates a computational and storage bottleneck for the system. By leveraging both sensors ’ abilities to communicate and their local computational power, our objective is to propose distributed algorithms for principal eigenvector and principal subspace tracking. We show that it is possible to have each sensor estimate only the corresponding entry of the principal eigenvector or corresponding row of thedimensional principal subspace matrix and do so by iterating a simple computation that combines data from its network neighbors only. This paper also examines the convergence properties of the proposed principal eigenvector and principal subspace tracking algorithms analytically and by simulations. Index Terms—Asynchronous time, average consensus, distributed algorithm, gossiping, signal detection, stochastic approximation, subspace estimation, subspace tracking, synchronous time. I.
DISTRIBUTED ADAPTIVE EIGENVECTOR ESTIMATION OF THE SENSOR SIGNAL COVARIANCE MATRIX IN A FULLY CONNECTED SENSOR NETWORK
, 2013
"... Distributed adaptive eigenvector estimation of the sensor signal covariance matrix in a fully connected sensor network ..."
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Distributed adaptive eigenvector estimation of the sensor signal covariance matrix in a fully connected sensor network
Experiments on Formation Switching for Mobile Robots
"... Abstract — In this paper, we address the problem of distributed role assignment for multiple mobile robots. This problem arises when a mobile robot in the team must decide what role to take on in a desired formation configuration. In some applications, in which the center and the orientation of a de ..."
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Abstract — In this paper, we address the problem of distributed role assignment for multiple mobile robots. This problem arises when a mobile robot in the team must decide what role to take on in a desired formation configuration. In some applications, in which the center and the orientation of a desired formation are not predetermined, the rotation and translation of the formation can be computed by using average consensus protocols. However, the conflict arises when the same role is assigned to more than one robot. This problem is resolved by using a negotiation strategy, while each assigned robot is traveling to the target position. We evaluate our proposed framework through two experiments on a team of physical nonholonomic mobile robots, i.e., (i) robots reconfigure themselves from one formation to another, and (ii) formation switching happens, while each robot is following a reference path. I.
Cloud KSVD: Computing dataadaptive representations in the cloud
"... Abstract—This paper studies the problem of dataadaptive representations for big, distributed data. It is assumed that a number of geographicallydistributed, interconnected sites have massive local data and they are interested in collaboratively learning a lowdimensional geometric structure under ..."
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Abstract—This paper studies the problem of dataadaptive representations for big, distributed data. It is assumed that a number of geographicallydistributed, interconnected sites have massive local data and they are interested in collaboratively learning a lowdimensional geometric structure underlying these data. In contrast to some of the previous works on subspace representations, this paper focuses on the geometric structure of a union of subspaces (UoS). Specifically, it proposes a distributed algorithm, termed as cloud KSVD, for learning a UoS structure underlying distributed data of interest. Cloud KSVD accomplishes the goal of collaborative dataadaptive representations without requiring communication of individual data samples between different sites. The paper also provides a partial analysis of cloud KSVD that gives insights into its convergence properties and deviations from a centralized solution in terms of properties of local data and topology of interconnections. Finally, it numerically illustrates the efficacy of cloud KSVD. I.