2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDASPS-3.4
Paper Title OPTIMAL TOA LOCALIZATION FOR MOVING SENSOR IN ASYMMETRIC NETWORK
Authors Sihao Zhao, Xiao-Ping Zhang, Ryerson University, Canada; Xiaowei Cui, Mingquan Lu, Tsinghua University, China
SessionASPS-3: IoT
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Applied Signal Processing Systems: Signal Processing over IoT [OTH-IoT]
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Abstract In a localization system based-on asymmetric network, only one of the anchor nodes (ANs) transmits signal. A sensor node (SN) receives it and then transmits signal that is received by all ANs to form time-of-arrival (TOA) measurements. SN localization is achieved based-on these TOA measurements along with the known AN positions. Existing work all assumes the SN is stationary. This will cause extra localization error for a moving SN. We develop an optimal localization method based-on maximum likelihood (ML) estimator, namely ML-LOC, utilizing information on the SN velocity and clock drift, to determine the position of a moving SN. We analyze its localization error and derive the Cramér-Rao lower bound (CRLB). Results from numerical simulations verify its optimal performance. We implement a prototype hardware localization system based-on consumer level ultra-wide band (UWB) chips. Experiments using the real system are carried out. Results validate the performance of the proposed method and show its feasibility in real-world applications.