Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1504/IJSNET.2009.026364 | IJSNET |
Keywords | Field | DocType |
localisation problem,accurate knowledge,micaz node,proposed iekf,nodes position,sensor networks localisation,interlaced extended kalman filter,centralised approach,new approach,prior knowledge,accurate estimation,extended kalman filter,sensor networks,ekf,sensor network | Extended Kalman filter,Computer science,Fault detection and isolation,Sensor array,Kalman filter,Ranging,Wireless sensor network,Distributed computing,Computational complexity theory,Computation | Journal |
Volume | Issue | ISSN |
5 | 3 | 1748-1279 |
Citations | PageRank | References |
15 | 0.67 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Gasparri | 1 | 52 | 2.69 |
| S. Panzieri | 2 | 110 | 13.09 |
| F. Pascucci | 3 | 58 | 3.88 |
| G. Ulivi | 4 | 239 | 45.88 |