Technical Program

Paper Detail

Session:Signal Processing for Wireless Sensor Networks II
Time:Wednesday, May 19, 13:51 - 14:08
Presentation: Special Session Lecture
Topic: Special Sessions: Signal Processing for Wireless Sensor Networks
Authors: Alexander Ihler; Massachusetts Institute of Technology 
 John Fisher; Massachusetts Institute of Technology 
 Randolph Moses; Ohio State University 
 Alan Willsky; Massachusetts Institute of Technology 
Abstract: Automatic self-calibration of ad-hoc sensor networks is a critical need for their use in military or civilian applications. In general, self-calibration involves the combination of absolute location information (e.g. GPS) with relative calibration information (e.g. estimated distance between sensors) over regions of the network. We formulate the self-calibration problem as a graphical model, enabling application of nonparametric belief propagation (NBP), a recent generalization of particle filtering, for both estimating sensor locations and representing location uncertainties. NBP has the advantage that it is easily implemented in a distributed fashion, can represent multi-modal uncertainty, and admits a wide variety of statistical models. This last point is particularly appealing in that it can be used to provide robustness against occasional high-variance (outlier) noise. We illustrate the performance of NBP using Monte Carlo analysis on an example network.

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