Nonparametric belief propagation for self-calibration in sensor networks

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. time delay or received signal strength between sensors) over regions of the network. Furthermore, it is generally desirable to distribute the computational burden across the network and minimize the amount of inter-sensor communication. We demonstrate that the information used for sensor calibration is fundamentally local with regard to the network topology and use this observation to reformulate the problem within a graphical model framework. We then demonstrate the utility 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, admits...

• Type: Conference
• Year: 2004
• Where: IPSN
• Authors: Alexander T. Ihler, John W. Fisher III, Randolph L. Moses, Alan S. Willsky
• Keywords: Ad-Hoc Sensor Networks, Belief Propagation, IPSN 2004, Sensor Network, Sensor Networks