Efficient Indoor Fingerprinting Localization Technique Using Regional Propagation Model
Summary :
The increasing demand of indoor location based service (LBS) has promoted the development of localization techniques. As an important alternative, fingerprinting localization technique can achieve higher localization accuracy than traditional trilateration and triangulation algorithms. However, it is computational expensive to construct the fingerprint database in the offline phase, which limits its applications. In this paper, we propose an efficient indoor positioning system that uses a new empirical propagation model, called regional propagation model (RPM), which is based on the cluster based propagation model theory. The system first collects the sparse fingerprints at some certain reference points (RPs) in the whole testing scenario. Then affinity propagation clustering algorithm operates on the sparse fingerprints to automatically divide the whole scenario into several clusters or sub-regions. The parameters of RPM are obtained in the next step and are further used to recover the entire fingerprint database. Finally, the location estimation is obtained through the weighted k-nearest neighbor algorithm (WkNN) in the online localization phase. We also theoretically analyze the localization accuracy of the proposed algorithm. The numerical results demonstrate that the proposed propagation model can predict the received signal strength (RSS) values more accurately than other models. Furthermore, experiments also show that the proposed positioning system achieves higher localization accuracy than other existing systems while cutting workload of fingerprint calibration by more than 50% in the offline phase.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E97-B No.8 pp.1728-1741
- Publication Date
- 2014/08/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E97.B.1728
- Type of Manuscript
- PAPER
- Category
- Sensing
Authors
Genming DING
Beijing Jiaotong University (BJTU)
Zhenhui TAN
Beijing Jiaotong University (BJTU)
Jinsong WU
Bell Laboratories
Jinbao ZHANG
BJTU
Keyword
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Genming DING, Zhenhui TAN, Jinsong WU, Jinbao ZHANG, "Efficient Indoor Fingerprinting Localization Technique Using Regional Propagation Model" in IEICE TRANSACTIONS on Communications,
vol. E97-B, no. 8, pp. 1728-1741, August 2014, doi: 10.1587/transcom.E97.B.1728.
Abstract: The increasing demand of indoor location based service (LBS) has promoted the development of localization techniques. As an important alternative, fingerprinting localization technique can achieve higher localization accuracy than traditional trilateration and triangulation algorithms. However, it is computational expensive to construct the fingerprint database in the offline phase, which limits its applications. In this paper, we propose an efficient indoor positioning system that uses a new empirical propagation model, called regional propagation model (RPM), which is based on the cluster based propagation model theory. The system first collects the sparse fingerprints at some certain reference points (RPs) in the whole testing scenario. Then affinity propagation clustering algorithm operates on the sparse fingerprints to automatically divide the whole scenario into several clusters or sub-regions. The parameters of RPM are obtained in the next step and are further used to recover the entire fingerprint database. Finally, the location estimation is obtained through the weighted k-nearest neighbor algorithm (WkNN) in the online localization phase. We also theoretically analyze the localization accuracy of the proposed algorithm. The numerical results demonstrate that the proposed propagation model can predict the received signal strength (RSS) values more accurately than other models. Furthermore, experiments also show that the proposed positioning system achieves higher localization accuracy than other existing systems while cutting workload of fingerprint calibration by more than 50% in the offline phase.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E97.B.1728/_p
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@ARTICLE{e97-b_8_1728,
author={Genming DING, Zhenhui TAN, Jinsong WU, Jinbao ZHANG, },
journal={IEICE TRANSACTIONS on Communications},
title={Efficient Indoor Fingerprinting Localization Technique Using Regional Propagation Model},
year={2014},
volume={E97-B},
number={8},
pages={1728-1741},
abstract={The increasing demand of indoor location based service (LBS) has promoted the development of localization techniques. As an important alternative, fingerprinting localization technique can achieve higher localization accuracy than traditional trilateration and triangulation algorithms. However, it is computational expensive to construct the fingerprint database in the offline phase, which limits its applications. In this paper, we propose an efficient indoor positioning system that uses a new empirical propagation model, called regional propagation model (RPM), which is based on the cluster based propagation model theory. The system first collects the sparse fingerprints at some certain reference points (RPs) in the whole testing scenario. Then affinity propagation clustering algorithm operates on the sparse fingerprints to automatically divide the whole scenario into several clusters or sub-regions. The parameters of RPM are obtained in the next step and are further used to recover the entire fingerprint database. Finally, the location estimation is obtained through the weighted k-nearest neighbor algorithm (WkNN) in the online localization phase. We also theoretically analyze the localization accuracy of the proposed algorithm. The numerical results demonstrate that the proposed propagation model can predict the received signal strength (RSS) values more accurately than other models. Furthermore, experiments also show that the proposed positioning system achieves higher localization accuracy than other existing systems while cutting workload of fingerprint calibration by more than 50% in the offline phase.},
keywords={},
doi={10.1587/transcom.E97.B.1728},
ISSN={1745-1345},
month={August},}
Copy
TY - JOUR
TI - Efficient Indoor Fingerprinting Localization Technique Using Regional Propagation Model
T2 - IEICE TRANSACTIONS on Communications
SP - 1728
EP - 1741
AU - Genming DING
AU - Zhenhui TAN
AU - Jinsong WU
AU - Jinbao ZHANG
PY - 2014
DO - 10.1587/transcom.E97.B.1728
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E97-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2014
AB - The increasing demand of indoor location based service (LBS) has promoted the development of localization techniques. As an important alternative, fingerprinting localization technique can achieve higher localization accuracy than traditional trilateration and triangulation algorithms. However, it is computational expensive to construct the fingerprint database in the offline phase, which limits its applications. In this paper, we propose an efficient indoor positioning system that uses a new empirical propagation model, called regional propagation model (RPM), which is based on the cluster based propagation model theory. The system first collects the sparse fingerprints at some certain reference points (RPs) in the whole testing scenario. Then affinity propagation clustering algorithm operates on the sparse fingerprints to automatically divide the whole scenario into several clusters or sub-regions. The parameters of RPM are obtained in the next step and are further used to recover the entire fingerprint database. Finally, the location estimation is obtained through the weighted k-nearest neighbor algorithm (WkNN) in the online localization phase. We also theoretically analyze the localization accuracy of the proposed algorithm. The numerical results demonstrate that the proposed propagation model can predict the received signal strength (RSS) values more accurately than other models. Furthermore, experiments also show that the proposed positioning system achieves higher localization accuracy than other existing systems while cutting workload of fingerprint calibration by more than 50% in the offline phase.
ER -
English
