Tensor-Aided Multipath Estimation

An effective multipath estimation method via spatial-temporal feature tensorization

Abstract

Realizing high-accuracy positioning faces tremendous challenges, where the multipath effect is one of the most influential factors. We propose a tensor-based algorithmic framework for localization in multipath environments, which achieves superior performance with low computational complexity. Specifically, we construct low-rank tensors to characterize sparse spatial-temporal features and separate coherent multipath signals hinging on the uniqueness of tensor decomposition. Compared with related works, our method does not rely on array configurations or signal structures, revealing its potential for broad use in multipath estimation.

Highlights

  • Propose a tensor-based algorithmic framework for localization in multipath environments, which exploits sparse spatio-temporal features of the received waveforms and achieves superior performance with low computational complexity.

  • Separate coherent multipath signals via low-rank tensor decomposition, where the block term decomposition tensors are constructed to characterize sparse spatio-temporal features and are separated hinging on the uniqueness of tensor decomposition.

Papers

  1. H. Zhao, M. Huang, and Y. Shen, “High-accuracy localization in multipath environments via spatio-temporal feature tensorization,” IEEE Trans. Wireless Commun., vol. 21, no 12, pp. 10576-10591, Dec. 2022.

  2. Y. Gong, H. Zhao, K. Hu, Q. Lu, and Y. Shen, “A multipath-aided localization method for MIMO-OFDM system via tensor decomposition,” IEEE Wireless Commun. Lett., vol. 11, no. 6, pp. 1225-1228, Jun. 2022.

  3. M. Huang, H. Zhao, and Y. Shen, “A multipath estimation method via block term decomposition for multi-carrier systems,” in Proc. IEEE Global Commun. Conf., Madrid, Spain, Dec. 2021, pp. 1–6.

  4. H. Zhao, Y. Gong, and Y. Shen, “A multipath separation method for network localization via tensor decomposition,” in Proc. IEEE Int. Conf. Commun., Montreal, Canada, Jun. 2021, pp. 1–6.