| Paper: | SS-5.4 | ||
| 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 | ||
| Title: | NONPARAMETRIC BELIEF PROPAGATION FOR SENSOR SELF-CALIBRATION | ||
| 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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