An Interlaced Extended Kalman Filter for sensor networks localisation
by A. Gasparri, S. Panzieri, F. Pascucci, G. Ulivi
International Journal of Sensor Networks (IJSNET), Vol. 5, No. 3, 2009

Abstract: Sensor networks have become a widely used technology for applications ranging from military surveillance to industrial fault detection. So far, the evolution in micro-electronics has made it possible to build networks of inexpensive nodes characterised by modest computation and storage capability as well as limited battery life. In such a context, having an accurate knowledge about nodes position is fundamental to achieve almost any task. Several techniques to deal with the localisation problem have been proposed in literature: most of them rely on a centralised approach, whereas others work in a distributed fashion. However, a number of approaches do require a prior knowledge of particular nodes, i.e. anchors, whereas others can face the problem without relying on this information. In this paper, a new approach based on an Interlaced Extended Kalman Filter (IEKF) is proposed: the algorithm, working in a distributed fashion, provides an accurate estimation of node poses with a reduced computational complexity. Moreover, no prior knowledge for any nodes is required to produce an estimation in a relative coordinate system. Exhaustive experiments, carried on MICAz nodes, are shown to prove the effectiveness of the proposed IEKF.

Online publication date: Mon, 08-Jun-2009

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Sensor Networks (IJSNET):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email