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Accuracy Sensing Parameter Estimation and Indoor Positioning in WiFi Systems via Tensor Analysis

Published: 12 June 2024 Publication History

Abstract

In this paper, we propose a tensor-based method for sensing parameter estimation and indoor positioning in WiFi systems. In general, the number of antennas deployed on WiFi sensors is no more than three and the multipath effect is aggravated in indoor environments. Without increasing the time overhead, spatial and temporal resources are scarce, which makes it difficult to satisfy the uniqueness of tensor decomposition. To address this problem, we design a new tensor reconstruction scheme by utilizing the abundant frequency resource in orthogonal frequency division multiplexing (OFDM). Based on the reconstructed tensor model, we develop iterative and non-iterative estimation algorithms to recover the accuracy sensing parameters. Then, the estimated sensing parameters related to the line-of-sight (LOS) path are used for indoor positioning. We also derive the Cramér Rao bound (CRB) as a performance benchmark. Simulation results show that the proposed method provides better estimation and positioning performance with lower complexity than the existing advanced methods.

References

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AIEE '24: Proceedings of the 2024 5th International Conference on Artificial Intelligence in Electronics Engineering
January 2024
89 pages
ISBN:9798400716850
DOI:10.1145/3658835
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 12 June 2024

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Author Tags

  1. CRB
  2. Indoor positioning
  3. Sensing parameter estimation
  4. Uniqueness of tensor decomposition
  5. WiFi

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