In this thesis, we consider the problem of decentralized estimation under communication
constraints in the context of Collaborative Signal and Information Processing. Motivated
by sensor network applications, a high volume of data collected at distinct locations and
possibly in diverse modalities together with the spatially distributed nature and the
resource limitations of the underlying system are of concern. Designing processing
schemes which match the constraints imposed by the system while providing a
reasonable accuracy has been a major challenge in which we are particularly interested
in the tradeoff between the estimation performance and the utilization of communications
subject to energy and bandwidth constraints.
One remarkable approach for decentralized inference in sensor networks is to exploit
graphical models together with message passing algorithms. In this framework, after the
so-called information graph of the problem is constructed, it is mapped onto the
underlying network structure which is responsible for delivering the messages in
accordance with the schedule of the inference algorithm. However it is challenging to
provide a design perspective that addresses the tradeoff between the estimation
accuracy and the cost of communications. Another approach has been performing the
estimation at a fusion center based on the quantized information provided by the
peripherals in which the fusion and quantization rules are sought while taking a restricted
set of the communication constraints into account.
We consider two classes of in-network processing strategies which cover a broad range
of constraints and yield tractable Bayesian risks that capture the cost of communications
as well as the penalty for estimation errors. A rigorous design setting is obtained in the
form of a constrained optimization problem utilizing the Bayesian risks. These
processing schemes have been previously studied together with the structures that the
solutions exhibit in the context of decentralized detection in which a decision out of
finitely many choices is made.
We adopt this framework for the estimation problem. However, for the case,
computationally infeasible solutions arise that involve integral operators that are
impossible to evaluate exactly in general. In order not to compromise the fidelity of the
model we develop an approximation framework using Monte Carlo methods and obtain
particle representations and approximate computational schemes for both the in-network
processing strategies and the solution schemes to the design problem. Doing that, we
can produce approximating strategies for decentralized estimation networks under
communication constraints captured by the framework including the cost. The proposed
Monte Carlo optimization procedures operate in a scalable and efficient manner and can
produce results for any family of distributions of concern provided that samples can be
produced from the marginals. In addition, this approach enables a quantification of the
tradeoff between the estimation accuracy and the cost of communications through
a parameterized Bayesian risk.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12611226/index.pdf |
| Date | 01 August 2009 |
| Creators | Uney, Murat |
| Contributors | Leblebicioglu, Kemal |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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