TY - GEN
T1 - Handling fingerprint sparsity for Wi-Fi based indoor localization in complex environments
AU - Li, Hao
AU - NG, Joseph K Y
AU - Liu, Kai
N1 - Funding Information:
This piece of research work is supported in part by the HKBU Research Centre for Ubiquitous Computing, the HKBU Institute of Computational and Theoretical Studies, and by the National Natural Science Foundation of China under Grant No.61872049 and No. 61572088; the Frontier Interdisciplinary Research Funds for the Central Universities (Project No. 2018CDQYJSJ0034); and Chongqing Application Foundation and Research in Cutting-edge Technologies (cstc2017jcyjAX0026).
Funding Information:
VII. ACKNOWLEDGEMENT This piece of research work is supported in part by the HKBU Research Centre for Ubiquitous Computing, the HKBU Institute of Computational and Theoretical Studies, and by the National Natural Science Foundation of China under Grant No.61872049 and No. 61572088; the Frontier Interdisciplinary Research Funds for the Central Universities (Project No. 2018CDQYJSJ0034); and Chongqing Application Foundation and Research in Cutting-edge Technologies (cstc2017jcyjAX0026).
PY - 2019/8
Y1 - 2019/8
N2 - In Wi-Fi based indoor localization, the fingerprint approach is one of the most popular solutions to achieving satisfactory positioning performance based on the requirements of many commercialized applications. However, the performance of such an approach heavily relies on the density of reference points collected offline. Previous efforts have tried to use regression based approaches to alleviate the influence caused by the sparsity of fingerprints and enhance the localization accuracy. Nevertheless, we observe that regression based methods are not applicable in complex environments. We first verify this observation based on experiments and give insight into the problem. Then a general framework is proposed to handle fingerprint sparsity in complex environments (HFSCE). In HFSCE, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is adopted to reduce the impact of complex indoor environments on regression. The purpose is to distinguish uncorrelated Received Signal Strength (RSS) data and cluster similar RSS data. After the clustering of RSS data, regression approaches are applied to estimate the continuous space and hence the localization model is built with finer pattern of the environment. Note that the proposed HFSCE framework can accommodate any regression model. In the online phase, we design an algorithm selection scheme, which chooses the most suitable localization approach and regression model for current RSS input. Series of experiments show our proposed approach has significant improvement in localization accuracy when compared with the state-of-the-art approaches.
AB - In Wi-Fi based indoor localization, the fingerprint approach is one of the most popular solutions to achieving satisfactory positioning performance based on the requirements of many commercialized applications. However, the performance of such an approach heavily relies on the density of reference points collected offline. Previous efforts have tried to use regression based approaches to alleviate the influence caused by the sparsity of fingerprints and enhance the localization accuracy. Nevertheless, we observe that regression based methods are not applicable in complex environments. We first verify this observation based on experiments and give insight into the problem. Then a general framework is proposed to handle fingerprint sparsity in complex environments (HFSCE). In HFSCE, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is adopted to reduce the impact of complex indoor environments on regression. The purpose is to distinguish uncorrelated Received Signal Strength (RSS) data and cluster similar RSS data. After the clustering of RSS data, regression approaches are applied to estimate the continuous space and hence the localization model is built with finer pattern of the environment. Note that the proposed HFSCE framework can accommodate any regression model. In the online phase, we design an algorithm selection scheme, which chooses the most suitable localization approach and regression model for current RSS input. Series of experiments show our proposed approach has significant improvement in localization accuracy when compared with the state-of-the-art approaches.
KW - DBSCAN
KW - Fingerprint sparsity
KW - Regression
KW - Wi-Fi indoor localization
UR - http://www.scopus.com/inward/record.url?scp=85083585172&partnerID=8YFLogxK
U2 - 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00210
DO - 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00210
M3 - Conference contribution
AN - SCOPUS:85083585172
T3 - Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
SP - 1109
EP - 1116
BT - Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
Y2 - 19 August 2019 through 23 August 2019
ER -