Statistical and Information-Theoretic Methods for Self-Organization and Fusion of Multimodal, Networked Sensors
The appeal of distributed sensing and computation is matched by the formidable challenges it presents in terms of estimation and communication. Applications range from military surveillance to collaborative office environments. Despite the attractiveness of exploiting networks of low-power and low-cost sensors, how to do so is a difficult problem. In this paper, we adopt a statistical viewpoint of such networks, and identify three key challenges. The first is to develop principled methods for low-level fusion of sensors measuring different modalities. We discuss an information-theoretic approach to sensor fusion, and present experimental results using audio and video data. The core component of this method is the learning of a nonparametric joint statistical model for the sensing modes. Secondly, we discuss how one might apply such a sensor fusion algorithm to acquire the relative geometry of a network of sensors using passively-sensed data. Specifically, we show how the fusion method previously developed can be used to find correspondences between pairs of long-baseline sensors. Finding such correspondences is, in general, the starting point for recovering the geometry. Finally, we discuss two iterative algorithms for performing inference on graphical models with cycles. Such models provide a flexible framework for constructing globally consistent statistical models from a set of local interactions. Importantly, the algorithms that we present allow information to be transmitted and processed in a distributed manner.