TY - GEN
T1 - Utilizing CSI and RSSI to Achieve High-Precision Outdoor Positioning
T2 - 2019 IEEE International Conference on Communications, ICC 2019
AU - Zhang, Hongbo
AU - Du, Hongwei
AU - Ye, Qiang
AU - Liu, Chuang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Location-Based Service (LBS) has been widely deployed. One of the key components of LBS is the positioning algorithm. For outdoor environments, the Global Positioning System (GPS) has been used as the default positioning scheme. However, GPS requires the line of sight to the satellites. When the line of sight is blocked, GPS simply stops working. To tackle the problem with GPS, varied WiFi-based positioning schemes have been proposed. However, the positioning precision of the existing methods is not satisfactory. In this paper, we present a high-precision positioning scheme named Deep Learning based Positioning (DLP). Technically, DLP utilizes both Received Signal Strength Indicator (RSSI) and Channel State Information (CSI) to improve the positioning precision. In detail, a deep neural network is used to model the received RSSI and CSI measurements, which leads to satisfactory positioning accuracy. Our experimental results acquired from a large-scale testbed indicate that DLP outperforms the existing positioning schemes in terms of positioning precision.
AB - Location-Based Service (LBS) has been widely deployed. One of the key components of LBS is the positioning algorithm. For outdoor environments, the Global Positioning System (GPS) has been used as the default positioning scheme. However, GPS requires the line of sight to the satellites. When the line of sight is blocked, GPS simply stops working. To tackle the problem with GPS, varied WiFi-based positioning schemes have been proposed. However, the positioning precision of the existing methods is not satisfactory. In this paper, we present a high-precision positioning scheme named Deep Learning based Positioning (DLP). Technically, DLP utilizes both Received Signal Strength Indicator (RSSI) and Channel State Information (CSI) to improve the positioning precision. In detail, a deep neural network is used to model the received RSSI and CSI measurements, which leads to satisfactory positioning accuracy. Our experimental results acquired from a large-scale testbed indicate that DLP outperforms the existing positioning schemes in terms of positioning precision.
UR - https://www.scopus.com/pages/publications/85070232938
U2 - 10.1109/ICC.2019.8761305
DO - 10.1109/ICC.2019.8761305
M3 - 会议稿件
AN - SCOPUS:85070232938
T3 - IEEE International Conference on Communications
BT - 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 May 2019 through 24 May 2019
ER -