Hybrid Bayesian fusion of range-based and sourceless location estimates under varying observability

Yadav, Nagesh; Bleakley, Chris J.

doi:10.1109/is.2012.6335119

Cited by 1 publication

(1 citation statement)

References 20 publications

(18 reference statements)

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“…In [11], the authors proposed a Bayesian fusion method for indoor localization using RSSI fingerprinting method in a Bluetooth networks. In [12], the authors devised a hybrid Bayesian approach for fusing rangebased and sourceless localization estimates in 3D localization under varying observation condition. The hybrid Bayesian approach uses a mixture of Single Hypothesis Tracking filtering (SHT) and Sequential Monte Carlo (SMC) filtering for accurate localization.…”

Section: Introductionmentioning

confidence: 99%

Robust indoor localization based on hybrid Bayesian graphical models

Kim

Lim

Hwang

et al. 2014

2014 IEEE Global Communications Conference

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In this paper, we study the problem of accurately estimating the location of indoor wireless devices which, due to its vast application areas, has continued to generate much interest both in the academia and industry. Specifically, we consider the location estimation problem and develop non-hybrid Bayesian location estimators for when received signal strength (RSS) and/or time of arrival (TOA) measurements between the target node (TN) and designated beacon nodes (BNs) in the network are obtainable. However, RSS-and TOA-based location estimators can be inaccurate when the RF-characteristics are not stable and time synchronization between the TN and BNs is not set up properly, respectively. To mitigate the disadvantages of these two location estimators, we propose the hybrid Bayesian location estimators. We show, through the results of extensive simulation experiments conducted that in comparison with the non-hybrid estimators, the hybrid Bayesian location estimators are more robust in the localization environment where RSS/TOA measurement precision varies.Index Terms-Localization, Bayesian estimation, received signal strength, time of arrival, time difference of arrival.

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Section: Introductionmentioning

confidence: 99%