Distributed Kalman Filter algorithms for self-localization of mobile devices

Authors:
Anne-Kathrin Hess;Anders Rantzer
Affiliations:
Technische Universität Berlin, Berlin, Germany;Lund University, Lund, Sweden
Venue:
Proceedings of the 13th ACM international conference on Hybrid systems: computation and control
Year:
2010

Citing 4
Cited 0

The Cricket location-support system

MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Tracking moving devices with the cricket location system

Proceedings of the 2nd international conference on Mobile systems, applications, and services
Extended Kalman Filter for wireless LAN based indoor positioning

Decision Support Systems
Distributing the Kalman Filter for Large-Scale Systems

IEEE Transactions on Signal Processing - Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper addresses the problem of self localization of mobile devices. In particular, each device combines noisy measurements of its absolute position with distance measurements to its neighbors. The communication topology is modeled by a graph. Both static and dynamic graph structures are investigated. The self-localization task is addressed using distributed Kalman Filters. First a filter is designed which uses only locally available measurements for state estimation. Secondly, a data fusion step is added to the filter. This allows the usage of more measurement information available in the network to improve the accuracy. When the graph is dynamic, a larger communication radius is necessary to ensure reliable performance.