TY - GEN
T1 - From relative azimuth to absolute location
T2 - 26th Annual International Conference on Mobile Computing and Networking, MobiCom 2020
AU - Liu, Xuefeng
AU - Yang, Tianye
AU - Tang, Shaojie
AU - Guo, Peng
AU - Niu, Jianwei
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/4/16
Y1 - 2020/4/16
N2 - Pyroelectric infrared (PIR) sensors are considered to be promising devices for device-free localization due to its advantages of low cost, energy efficiency, and the immunity from multi-path fading. However, most of the existing PIR-based localization systems only utilize the binary information of PIR sensors and therefore require a large number of carefully deployed PIR sensors. A few works directly map the raw data of PIR sensors to one's location using machine learning approaches. However, these data-driven approaches require abundant training data and suffer from environmental change. In this paper, we propose PIRATES, a PIR-based device-free localization system based on the raw data of PIR sensors. The key of PIRATES is to extract a new type of location information called azimuth change. The extraction of the azimuth change relies on the physical properties of PIR sensors. Therefore, no abundant training data are needed and the system is robust to environmental change. Through experiments, we demonstrate that PIRATES can achieve higher localization accuracy than the state-of-the-art approaches. In addition, the information of the azimuth change can be easily incorporated with other information of PIR signals (e.g. amplitude) to improve the localization accuracy.
AB - Pyroelectric infrared (PIR) sensors are considered to be promising devices for device-free localization due to its advantages of low cost, energy efficiency, and the immunity from multi-path fading. However, most of the existing PIR-based localization systems only utilize the binary information of PIR sensors and therefore require a large number of carefully deployed PIR sensors. A few works directly map the raw data of PIR sensors to one's location using machine learning approaches. However, these data-driven approaches require abundant training data and suffer from environmental change. In this paper, we propose PIRATES, a PIR-based device-free localization system based on the raw data of PIR sensors. The key of PIRATES is to extract a new type of location information called azimuth change. The extraction of the azimuth change relies on the physical properties of PIR sensors. Therefore, no abundant training data are needed and the system is robust to environmental change. Through experiments, we demonstrate that PIRATES can achieve higher localization accuracy than the state-of-the-art approaches. In addition, the information of the azimuth change can be easily incorporated with other information of PIR signals (e.g. amplitude) to improve the localization accuracy.
KW - PIR sensors
KW - device-free localization
KW - inverse filter
KW - particle filter
UR - https://www.scopus.com/pages/publications/85086143226
U2 - 10.1145/3372224.3380878
DO - 10.1145/3372224.3380878
M3 - Conference contribution
AN - SCOPUS:85086143226
T3 - Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM
SP - 1
EP - 13
BT - Proceedings of the 26th Annual International Conference on Mobile Computing and Networking, MobiCom 2020
PB - Association for Computing Machinery
Y2 - 21 September 2020 through 25 September 2020
ER -