TDLoc: Passive Localization for MIMO-OFDM System via Tensor Decomposition

• Published in: IEEE internet of things journal Vol. 10; no. 23; p. 1
• Main Authors: Zhao, Bobai, Hu, Keke, Wen, Fuxi, Cui, Shulin, Shen, Yuan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Antenna arrays; Channel estimation; Cramer-Rao bounds; Fingerprint recognition; Localization; Location awareness; Lower bounds; MIMO communication; MIMO-OFDM; Multipath components; Multiple signal classification; Orthogonal Frequency Division Multiplexing; Parameter estimation; Passive localization; Performance evaluation; Relativistic velocity; Tensor decomposition; Tensors; Tracking systems; Trajectory
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3283991

Passive localization is an important aspect of integrated sensing and communication (ISAC). However, it is challenging to estimate the target position and velocity accurately from the receiving signals due to complex multipath propagation. This paper presents TDLoc, a multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) based passive localization and tracking system, using channel state information (CSI). We first proposed a fourth-order tensor model that contains angle-of-departure (AoD), angle-of-arrival (AoA), time-of-flight (ToF), and Doppler frequency shifts (DFS) information, followed by developing an efficient joint estimation algorithm. We also show that with more than one pair of transceivers, our method can obtain the target velocity from the relativistic Doppler effects, leading to additional DFS-based trajectory information. Moreover, the Cramér-Rao lower bound (CRLB) for multipath parameter estimation and positioning is derived for performance evaluation. Numerical results show that TDLoc outperforms state-of-the-art methods in terms of localization accuracy.