IEEE Access (Jan 2020)

Tensor-Based Framework With Model Order Selection and High Accuracy Factor Decomposition for Time-Delay Estimation in Dynamic Multipath Scenarios

  • Mateus Da Rosa Zanatta,
  • Joao Paulo Carvalho Lustosa Da Costa,
  • Felix Antreich,
  • Martin Haardt,
  • Gordon Elger,
  • Fabio Lucio Lopes De Mendonca,
  • Rafael Timoteo De Sousa

DOI
https://doi.org/10.1109/ACCESS.2020.3024597
Journal volume & issue
Vol. 8
pp. 174931 – 174942

Abstract

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Global Navigation Satellite Systems (GNSS) are crucial for applications that demand very accurate positioning. Tensor-based time-delay estimation methods, such as CPD-GEVD, DoA/KRF, and SECSI, combined with the GPS3 L1C signal, are capable of, significantly, mitigating the positioning degradation caused by multipath components. However, even though these schemes require an estimated model order, they assume that the number of multipath components is constant. In GNSS applications, the number of multipath components is time-varying in dynamic scenarios. Thus, in this paper, we propose a tensor-based framework with model order selection and high accuracy factor decomposition for time-delay estimation in dynamic multipath scenarios. Our proposed approach exploits the estimates of the model order for each slice by grouping the data tensor slices into sub-tensors to provide high accuracy factor decomposition. We further enhance the proposed approach by incorporating the tensor-based Multiple Denoising (MuDe).

Keywords