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Tensor-Based Subspace Tracking for Time-Delay Estimation in GNSS Multi-Antenna Receivers.

Caio C R Garcez1, Daniel Valle de Lima1, Ricardo Kehrle Miranda2

  • 1Department of Electrical Engineering, University of Brasília, 70910-900 Brasília, Brazil.

Sensors (Basel, Switzerland)
|November 24, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a tensor-based subspace tracking framework to improve Global Navigation Satellite Systems (GNSS) receivers. It significantly reduces computational complexity for accurate real-time time-delay estimation.

Keywords:
GNSS receiverstensor-based subspace estimationtime-delay estimationuniform linear array

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Area of Science:

  • Signal Processing
  • Geomatics Engineering
  • Navigation Systems

Background:

  • Global Navigation Satellite Systems (GNSS) receivers face accuracy degradation due to multipath interference and noise.
  • Multi-antenna receivers and tensor-based approaches like Parallel Factor Analysis (PARAFAC) show promise for mitigating these issues.

Purpose of the Study:

  • To develop a computationally efficient framework for accurate time-delay estimation in GNSS receivers.
  • To reduce the high computational complexity associated with current state-of-the-art methods.

Main Methods:

  • Proposed a novel tensor-based subspace tracking framework.
  • Leveraged Parallel Factor Analysis (PARAFAC) models for tensor decomposition.
  • Focused on reducing computational load compared to sample-by-sample Singular Value Decomposition (SVD).

Main Results:

  • The proposed framework significantly reduces computational complexity.
  • Maintains high accuracy in time-delay estimation for GNSS receivers.
  • Enables real-time application feasibility for advanced GNSS processing.

Conclusions:

  • Tensor-based subspace tracking offers an efficient solution for GNSS multipath mitigation.
  • The developed framework enhances the practicality of high-accuracy GNSS positioning.
  • This approach is crucial for advancing real-time navigation system performance.