TY - GEN
T1 - A proactive indoor positioning system in randomly deployed dense WiFi networks
AU - Ko, Chun Hsien
AU - Wu, Sau-Hsuan
PY - 2016/1/1
Y1 - 2016/1/1
N2 - A new approach for indoor positioning is presented, aimed at designing a WiFi positioning system that is feasible and convenient for both service providers and end users. In the proposed approach, only access points (APs) need to collect the received signal strengthes (RSS) of mobile devices, and use these RSS samples to jointly estimate the devices' locations. To enhance the accuracy of positioning, the relationship between the RSS samples and their geometrical locations is explored, leading to a sparse Bayesian model for the radio power map of the RSS observations of each AP. With more than 20 training anchors, the accuracy of the proposed model-based positioning method can be lower than 3.4 meters in an indoor space with only 4 randomly deployed APs, which outperforms the fingerprinting method by 0.4 meter. Extensive experimental results also verify that the proposed positioning service can offer considerable accuracy with only limited efforts in training, suggesting that the prototype is realistic for randomly deployed dense WiFi networks.
AB - A new approach for indoor positioning is presented, aimed at designing a WiFi positioning system that is feasible and convenient for both service providers and end users. In the proposed approach, only access points (APs) need to collect the received signal strengthes (RSS) of mobile devices, and use these RSS samples to jointly estimate the devices' locations. To enhance the accuracy of positioning, the relationship between the RSS samples and their geometrical locations is explored, leading to a sparse Bayesian model for the radio power map of the RSS observations of each AP. With more than 20 training anchors, the accuracy of the proposed model-based positioning method can be lower than 3.4 meters in an indoor space with only 4 randomly deployed APs, which outperforms the fingerprinting method by 0.4 meter. Extensive experimental results also verify that the proposed positioning service can offer considerable accuracy with only limited efforts in training, suggesting that the prototype is realistic for randomly deployed dense WiFi networks.
KW - Indoor Positioning
KW - Location Based Service (LBS)
KW - Radio Power Map (PRM)
KW - Sparse Bayesian Learning
KW - WiFi
UR - http://www.scopus.com/inward/record.url?scp=85015361008&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2016.7841536
DO - 10.1109/GLOCOM.2016.7841536
M3 - Conference contribution
AN - SCOPUS:85015361008
T3 - 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings
BT - 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 59th IEEE Global Communications Conference, GLOBECOM 2016
Y2 - 4 December 2016 through 8 December 2016
ER -