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Self-Position Determination Based on Array Signal Subspace Fitting under Multipath Environments.

Zhongkang Cao1, Pan Li1, Wanghao Tang1

  • 1College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

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|December 9, 2023
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Summary
This summary is machine-generated.

This study introduces a novel array signal subspace fitting method to accurately estimate vehicle positions using signal data, even in challenging multipath environments. The method effectively suppresses non-line-of-sight errors, improving positioning precision and reducing computational load.

Keywords:
array signal processingmultipath environmentnoncooperative signalself-position determination

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Area of Science:

  • Signal Processing
  • Navigation Systems
  • Array Signal Processing

Background:

  • Vehicle positioning traditionally relies on satellite navigation, but faces challenges in multipath environments.
  • Existing array self-position determination methods are prone to failure due to multipath interference.

Purpose of the Study:

  • To propose an array signal subspace fitting method for robust vehicle positioning.
  • To suppress multipath effects and non-line-of-sight (NLOS) components in signal reception.
  • To enhance positioning accuracy and reduce computational complexity.

Main Methods:

  • Estimating signal incidence angles using enhanced spatial smoothing and root multiple signal classification (Root-MUSIC).
  • Employing K-means clustering to distinguish non-line-of-sight (NLOS) signals from multipath components.
  • Utilizing a signal subspace fitting (SSF) function with a P matrix to mitigate NLOS errors and reduce search area.

Main Results:

  • The proposed method demonstrates superior NLOS component suppression compared to C-matrix, oblique projection, ISF, MUSIC, and SSF.
  • Achieved a reduction in computational complexity by at least 7.64dB.
  • Improved estimation accuracy by 3.10dB over clustering algorithms and 11.77dB over MUSIC, ISF, and SSF in multipath conditions.

Conclusions:

  • The developed array signal subspace fitting method offers enhanced performance for vehicle positioning in multipath environments.
  • The integration of K-means clustering and SSF significantly improves accuracy and efficiency.
  • This approach provides a reliable alternative to satellite navigation for precise vehicle localization.