IEICE TRANSACTIONS on Communications
Indoor Fingerprinting Localization and Tracking System Using Particle Swarm Optimization and Kalman Filter
Summary :
The indoor fingerprinting localization technology has received more attention in recent years due to the increasing demand of the indoor location based services (LBSs). However, a high quality of the LBS requires a positioning solution with high accuracy and low computational complexity. The particle swarm optimization (PSO) technique, which emulates the social behavior of a flock of birds to search for the optimal solution of a special problem, can provide attractive performance in terms of accuracy, computational efficiency and convergence rate. In this paper, we adopt the PSO algorithm to estimate the location information. First, our system establishes a Bayesian-rule based objective function. It then applies PSO to identify the optimal solution. We also propose a hybrid access point (AP) selection method to improve the accuracy, and analyze the effects of the number and the initial positions of particles on the localization performance. In order to mitigate the estimation error, we use the Kalman Filter to update the initial estimated location via the PSO algorithm to track the trail of the mobile user. Our analysis indicates that our method can reduce the computational complexity and improve the real-time performance. Numerous experiments also demonstrate that our proposed localization and tracking system achieve higher localization accuracy than existing systems.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E98-B No.3 pp.502-514
- Publication Date
- 2015/03/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E98.B.502
- Type of Manuscript
- PAPER
- Category
- Sensing
Authors
Genming DING
Beijing Jiaotong University (BJTU)
Zhenhui TAN
Beijing Jiaotong University (BJTU)
Jinsong WU
Bell Laboratories
Jinshan ZENG
Xi'an Jiaotong University
Lingwen ZHANG
Beijing Jiaotong University (BJTU)
Keyword
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Genming DING, Zhenhui TAN, Jinsong WU, Jinshan ZENG, Lingwen ZHANG, "Indoor Fingerprinting Localization and Tracking System Using Particle Swarm Optimization and Kalman Filter" in IEICE TRANSACTIONS on Communications,
vol. E98-B, no. 3, pp. 502-514, March 2015, doi: 10.1587/transcom.E98.B.502.
Abstract: The indoor fingerprinting localization technology has received more attention in recent years due to the increasing demand of the indoor location based services (LBSs). However, a high quality of the LBS requires a positioning solution with high accuracy and low computational complexity. The particle swarm optimization (PSO) technique, which emulates the social behavior of a flock of birds to search for the optimal solution of a special problem, can provide attractive performance in terms of accuracy, computational efficiency and convergence rate. In this paper, we adopt the PSO algorithm to estimate the location information. First, our system establishes a Bayesian-rule based objective function. It then applies PSO to identify the optimal solution. We also propose a hybrid access point (AP) selection method to improve the accuracy, and analyze the effects of the number and the initial positions of particles on the localization performance. In order to mitigate the estimation error, we use the Kalman Filter to update the initial estimated location via the PSO algorithm to track the trail of the mobile user. Our analysis indicates that our method can reduce the computational complexity and improve the real-time performance. Numerous experiments also demonstrate that our proposed localization and tracking system achieve higher localization accuracy than existing systems.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E98.B.502/_p
Copy
@ARTICLE{e98-b_3_502,
author={Genming DING, Zhenhui TAN, Jinsong WU, Jinshan ZENG, Lingwen ZHANG, },
journal={IEICE TRANSACTIONS on Communications},
title={Indoor Fingerprinting Localization and Tracking System Using Particle Swarm Optimization and Kalman Filter},
year={2015},
volume={E98-B},
number={3},
pages={502-514},
abstract={The indoor fingerprinting localization technology has received more attention in recent years due to the increasing demand of the indoor location based services (LBSs). However, a high quality of the LBS requires a positioning solution with high accuracy and low computational complexity. The particle swarm optimization (PSO) technique, which emulates the social behavior of a flock of birds to search for the optimal solution of a special problem, can provide attractive performance in terms of accuracy, computational efficiency and convergence rate. In this paper, we adopt the PSO algorithm to estimate the location information. First, our system establishes a Bayesian-rule based objective function. It then applies PSO to identify the optimal solution. We also propose a hybrid access point (AP) selection method to improve the accuracy, and analyze the effects of the number and the initial positions of particles on the localization performance. In order to mitigate the estimation error, we use the Kalman Filter to update the initial estimated location via the PSO algorithm to track the trail of the mobile user. Our analysis indicates that our method can reduce the computational complexity and improve the real-time performance. Numerous experiments also demonstrate that our proposed localization and tracking system achieve higher localization accuracy than existing systems.},
keywords={},
doi={10.1587/transcom.E98.B.502},
ISSN={1745-1345},
month={March},}
Copy
TY - JOUR
TI - Indoor Fingerprinting Localization and Tracking System Using Particle Swarm Optimization and Kalman Filter
T2 - IEICE TRANSACTIONS on Communications
SP - 502
EP - 514
AU - Genming DING
AU - Zhenhui TAN
AU - Jinsong WU
AU - Jinshan ZENG
AU - Lingwen ZHANG
PY - 2015
DO - 10.1587/transcom.E98.B.502
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E98-B
IS - 3
JA - IEICE TRANSACTIONS on Communications
Y1 - March 2015
AB - The indoor fingerprinting localization technology has received more attention in recent years due to the increasing demand of the indoor location based services (LBSs). However, a high quality of the LBS requires a positioning solution with high accuracy and low computational complexity. The particle swarm optimization (PSO) technique, which emulates the social behavior of a flock of birds to search for the optimal solution of a special problem, can provide attractive performance in terms of accuracy, computational efficiency and convergence rate. In this paper, we adopt the PSO algorithm to estimate the location information. First, our system establishes a Bayesian-rule based objective function. It then applies PSO to identify the optimal solution. We also propose a hybrid access point (AP) selection method to improve the accuracy, and analyze the effects of the number and the initial positions of particles on the localization performance. In order to mitigate the estimation error, we use the Kalman Filter to update the initial estimated location via the PSO algorithm to track the trail of the mobile user. Our analysis indicates that our method can reduce the computational complexity and improve the real-time performance. Numerous experiments also demonstrate that our proposed localization and tracking system achieve higher localization accuracy than existing systems.
ER -
English
