Paper Info
Title
Mobility improves LMI-based cooperative indoor localization
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.
Year
DOI
Venue
2015
10.1109/WCNC.2015.7127811
WCNC
Keywords
Field
DocType
gaussian-newton algorithm,kalman filter,indoor localization,linear matrix inequality,mobility,received signal strength
Computer science,Real-time computing,Moving horizon estimation
Conference
ISSN
Citations 
PageRank 
1525-3511
4
0.47
References 
Authors
7
6
Name
Order
Citations
PageRank
Xuyu Wang126520.80
Hui Zhou2121.01
Shiwen Mao32816192.93
Santosh Pandey41145.17
Prathima Agrawal51854277.08
David M. Bevly618827.06