Sequential Monte Carlo filtering for location estimation in indoor wireless environments

Authors:
Jihoon Ryoo;Hyunjun Choi;Hwangnam Kim
Affiliations:
Microsoft Research Asia, Beijing, China;School of Electrical Engineering, Korea University, Korea;School of Electrical Engineering, Korea University, Korea
Venue:
CCNC'10 Proceedings of the 7th IEEE conference on Consumer communications and networking conference
Year:
2010

Citing 1
Cited 1

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Abstract

In this paper, we propose a distributed, infrastructure-free algorithm for supporting self-localization and location-tracking of portable devices in home networks that do not rely on any positioning infrastructure, such as GPS (Global Positioning System). The proposed algorithm employs the received signal strength (RSS) to estimate the current position of each portable device and then elaborates the position with the box-based sequential Monte Carlo (BSMC) method. Simulation results indicate that the proposed algorithm is superior to the well-received Centroid algorithm [1] in terms of the distance estimation error.