Mobility improves LMI-based cooperative indoor localization. Wang, X., Zhou, H., Mao, S., Pandey, S., Agrawal, P., & Bevly, D. M. In 2015 IEEE Wireless Communications and Networking Conference (WCNC), pages 2215–2220, March, 2015. ISSN: 1558-2612![Mobility improves LMI-based cooperative indoor localization [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex With the proliferation of mobile devices such as smartphones, an interesting problem is how to make use them to improve the accuracy of localization in indoor environments. In this paper, we develop a novel cooperative localization scheme exploiting mobility in the indoor environment. The problem is formulated as a semidefinite program (SDP) using Linear Matrix Inequality (LMI). With the proposed approach, mobile users utilize their top RSS measurements for distance estimation and to mitigate the the shadowing effect found in indoor environments. In addition, we utilize the estimated position for a user from the last time slot as a virtual access point (AP) to obtain the next position estimation, by utilizing the inertial measurement unit (IMU) data from smartphones. To better take advantage of the moving direction and velocity information provided by the smartphones, we next apply Kalman filter to further mitigate the errors in estimated positions. Simulation results confirm that both the mean error and variance can be effectively reduced by exploiting IMU data and Kalman filter.
@inproceedings{wang_mobility_2015,
title = {Mobility improves {LMI}-based cooperative indoor localization},
url = {https://ieeexplore.ieee.org/document/7127811/;jsessionid=9D59D8946E719F5CF86D9860D7531EA2},
doi = {10.1109/WCNC.2015.7127811},
abstract = {With the proliferation of mobile devices such as smartphones, an interesting problem is how to make use them to improve the accuracy of localization in indoor environments. In this paper, we develop a novel cooperative localization scheme exploiting mobility in the indoor environment. The problem is formulated as a semidefinite program (SDP) using Linear Matrix Inequality (LMI). With the proposed approach, mobile users utilize their top RSS measurements for distance estimation and to mitigate the the shadowing effect found in indoor environments. In addition, we utilize the estimated position for a user from the last time slot as a virtual access point (AP) to obtain the next position estimation, by utilizing the inertial measurement unit (IMU) data from smartphones. To better take advantage of the moving direction and velocity information provided by the smartphones, we next apply Kalman filter to further mitigate the errors in estimated positions. Simulation results confirm that both the mean error and variance can be effectively reduced by exploiting IMU data and Kalman filter.},
urldate = {2024-06-20},
booktitle = {2015 {IEEE} {Wireless} {Communications} and {Networking} {Conference} ({WCNC})},
author = {Wang, Xuyu and Zhou, Hui and Mao, Shiwen and Pandey, Santosh and Agrawal, Prathima and Bevly, David M.},
month = mar,
year = {2015},
note = {ISSN: 1558-2612},
keywords = {Accuracy, Estimation, Gaussian-Newton algorithm, Indoor environments, Kalman filter, Kalman filters, Mobile communication, Smart phones, Standards, indoor localization, linear matrix inequality, mobility, received signal strength},
pages = {2215--2220},
}
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With the proposed approach, mobile users utilize their top RSS measurements for distance estimation and to mitigate the the shadowing effect found in indoor environments. In addition, we utilize the estimated position for a user from the last time slot as a virtual access point (AP) to obtain the next position estimation, by utilizing the inertial measurement unit (IMU) data from smartphones. To better take advantage of the moving direction and velocity information provided by the smartphones, we next apply Kalman filter to further mitigate the errors in estimated positions. 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