Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 5:00 PM ET (12:00 - 21:00 UTC). We apologize for the inconvenience.
By Topic

Estimating a random field in sensor networks using quantized spatially correlated data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Dogandzic, A. ; ECpE Dept., Iowa State Univ., Ames, IA ; Kun Qiu

We consider a fusion sensor-network architecture where sensor-processor elements (nodes) observe a spatially correlated random field within a region of interest and transmit quantized observations to a fusion center. The fusion center provides feedback by broadcasting summary information to the nodes. We assume that the observations follow a linear-regression model with known field correlations and propose a Bayesian framework for adaptive quantization, fusion-center feedback, and estimation of the field and its parameters. We consider local quantile and Lloyd-Max quantizers at the nodes; both quantization schemes are based on approximate predictive measurement distributions, constructed using the feedback information from the fusion center. We also apply our estimation approach to the no-feedback scenario and present numerical examples demonstrating the performance of the proposed methods.

Published in:

Signals, Systems and Computers, 2008 42nd Asilomar Conference on

Date of Conference:

26-29 Oct. 2008