Nonlinear Information Filtering for Distributed Multisensor Data Fusion

The information filter has evolved into a key tool for distributed and decentralized multisensor estimation and control. Essentially, it is an algebraical reformulation of the Kalman filter and provides estimates on the information about an uncertain state rather than on a state itself. Whereas many practicable Kalman filtering techniques for nonlinear system and sensor models have been developed, approaches towards nonlinear information filtering are still scarce and limited. In order to deal with nonlinear systems and sensors, this paper derives an approximation technique for arbitrary probability densities that provides the same distributable fusion structure as the linear information filter. The presented approach not only constitutes a nonlinear version of the information filter, but it also points the direction to a Hilbert space structure on probability densities, whose vector space operations correspond to the fusion and weighting of information

A Distributed Extended Information Filter for Self-Localization in Sensor Networks

In this paper the Self-Localization problem for Sensor Networks is addressed. Given a set of nodes deployed in an environment, self-localization consists of finding out the location of all nodes in regard to any topology or metric of interest. Nodes are assumed to be equipped with a sensor board able to provide these inter-node distances. In addition, a few nodes are assumed to be equipped with some absolute position devices. According to this scenario, a new distributed algorithm based on an Extended Information Filter is proposed. This algorithm provides an accurate estimation of node positions with a reasonable computational complexity, even when in presence of noisy measurements. Real experiments, carried out by exploiting Micaz Motes platforms, have been performed to show the effectiveness of the proposed technique