1,212 research outputs found
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
Distributed fault estimation for linear systems with actuator faults
This article investigates the problem of designing a distributed fault estimation observer (DFEO) for a given linear time invariant observed system with disturbances. The DFEO consists of a network of local fault estimation observers. The local observers at the network nodes are physically distributed and hence each of them has access to only part of the output of the observed system. Each local fault estimation observer communicates with its neighbors as prescribed by the given network graph. Both full order and reduced order DFEO's are presented in this article. A systematic design procedure for DFEO gains is addressed, enabling the estimation error dynamics to be robust against the effects of the external process disturbance and the derivative of the fault. The numerical design of our DFEO is amounts to solving an optimization problem with constraints of a bank of linear matrix inequalities. Finally, we illustrate the effectiveness of the proposed distributed fault estimation approach by means of a number of simulation results
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