A novel real time method of signal strength based indoor localization

Authors:
Letian Ye;Zhi Geng;Lingzhou Xue;Zhihai Liu
Affiliations:
School of Mathematics Sciences, LMAM, Peking University, Beijing, China;School of Mathematics Sciences, LMAM, Peking University, Beijing, China;School of Mathematics Sciences, LMAM, Peking University, Beijing, China;School of Mathematics Sciences, LMAM, Peking University, Beijing, China
Venue:
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Year:
2007

Citing 3
Cited 0

Localization from mere connectivity

Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Gaussian Markov Random Fields: Theory And Applications (Monographs on Statistics and Applied Probability)

Gaussian Markov Random Fields: Theory And Applications (Monographs on Statistics and Applied Probability)
A new location estimation system for wireless networks based on linear discriminant functions and hidden Markov models

EURASIP Journal on Applied Signal Processing

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Abstract

Localization using wireless signal is a hot field now, and the real time indoor localization is a difficult problem for its complex and sensitive to the environment. This paper proposes a method based on grid to convert global to local. Based on the Markov random field, we convert efficiently signals between different environments and achieve high precision and fast speed. The paper also discusses influence of multiple signals to location precision, explains that multiple sets of signal can be used greatly to improve localization precision. To reduce the number of supervised grids in learning data required by the grid-matching algorithm, this paper presents a method which combines the grid matching and the signal strength model. First the position is localized by the grid-matching method and then its location is refined by using the signal strength model in the local area.