TY - JOUR
T1 - Robust Sub-Meter Level Indoor Localization with a Single WiFi Access Point-Regression Versus Classification
AU - Xiang, Chenlu
AU - Zhang, Shunqing
AU - Xu, Shugong
AU - Chen, Xiaojing
AU - Cao, Shan
AU - Alexandropoulos, George C.
AU - Lau, Vincent K.N.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019
Y1 - 2019
N2 - Precise indoor localization is an increasingly demanding requirement for various emerging applications, like Virtual/Augmented reality and personalized advertising. Current indoor environments are equipped with pluralities of WiFi access points (APs), whose deployment is expected to be massive in the future enabling highly precise localization approaches. Though the conventional model-based localization schemes have achieved sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is usually significant, especially in the current multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In order to address this issue, model-free localization techniques using deep learning frameworks have been lately proposed, where mainly classification methods were applied. In this paper, instead of classification based mechanism, we propose a logistic regression based scheme with the deep learning framework, combined with Cramér-Rao lower bound (CRLB) assisted robust training, which achieves more robust sub-meter level accuracy (0.97m median distance error) in the standard laboratory environment and maintains reasonable online prediction overhead under the single WiFi AP settings.
AB - Precise indoor localization is an increasingly demanding requirement for various emerging applications, like Virtual/Augmented reality and personalized advertising. Current indoor environments are equipped with pluralities of WiFi access points (APs), whose deployment is expected to be massive in the future enabling highly precise localization approaches. Though the conventional model-based localization schemes have achieved sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is usually significant, especially in the current multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In order to address this issue, model-free localization techniques using deep learning frameworks have been lately proposed, where mainly classification methods were applied. In this paper, instead of classification based mechanism, we propose a logistic regression based scheme with the deep learning framework, combined with Cramér-Rao lower bound (CRLB) assisted robust training, which achieves more robust sub-meter level accuracy (0.97m median distance error) in the standard laboratory environment and maintains reasonable online prediction overhead under the single WiFi AP settings.
KW - channel state information
KW - deep learning
KW - Indoor localization
KW - logistic regression
KW - WiFi
UR - http://www.scopus.com/inward/record.url?scp=85078947883&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2946271
DO - 10.1109/ACCESS.2019.2946271
M3 - Article
AN - SCOPUS:85078947883
SN - 2169-3536
VL - 7
SP - 146309
EP - 146321
JO - IEEE Access
JF - IEEE Access
M1 - 8862951
ER -