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Reliable Indoor Pseudolite Positioning Based on a Robust Estimation and Partial Ambiguity Resolution Method.

Xin Li1, Guanwen Huang1, Peng Zhang2

  • 1College of Geology Engineering and Geomantic, Chang'an University, Xi'an 710054, China.

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|August 28, 2019
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Summary
This summary is machine-generated.

A new robust unscented Kalman filter (RUKF) and partial ambiguity resolution (PAR) algorithm improve indoor pseudolite (PL) positioning accuracy and reliability. This method effectively handles abnormal observations, outperforming standard UKF for both code-based and phase-based positioning.

Keywords:
differential PL (DPL)indoor pseudolite (PL)partial ambiguity resolution (PAR)real-time kinematic (RTK) positioningrobust unscented kalman filter (RUKF)standard unscented kalman filter (SUKF)

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Area of Science:

  • Geomatics Engineering
  • Navigation Systems
  • Signal Processing

Background:

  • Indoor pseudolite (PL) positioning faces challenges with complex environments leading to abnormal observations.
  • Standard unscented Kalman filter (SUKF) struggles with frequent abnormal data, impacting accuracy and reliability, especially in high-precision phase-based positioning.
  • Ambiguity resolution (AR) is critical for high-precision positioning but is hindered by abnormal observations.

Purpose of the Study:

  • To introduce a robust unscented Kalman filter (RUKF) and partial ambiguity resolution (PAR) algorithm for indoor PL positioning.
  • To enhance the accuracy and reliability of indoor PL positioning systems, particularly under adverse observational conditions.
  • To address the limitations of SUKF in handling abnormal observations and facilitating AR.

Main Methods:

  • Utilized the unscented Kalman filter (UKF) for parameter estimation.
  • Developed anomaly recognition statistics and an optimal ambiguity subset using posterior residuals for the PAR algorithm.
  • Implemented the IGGIII scheme to mitigate abnormal observation influence and employed PAR for AR failures.

Main Results:

  • RUKF-based indoor differential PL (DPL) positioning achieved decimeter-level accuracy improvements over SUKF, especially with gross errors.
  • RUKF successfully identified centimeter-level anomalous observations in real-time kinematic (RTK) positioning, enhancing accuracy compared to SUKF.
  • The combination of RUKF and PAR enabled successful PL-AR for selected ambiguity subsets, significantly improving positioning accuracy and reliability in the presence of large gross errors.

Conclusions:

  • The proposed RUKF and PAR algorithm demonstrates significant resistance to abnormal observations in indoor PL positioning.
  • This robust approach offers superior performance over conventional methods for both DPL and high-precision RTK indoor positioning.
  • The algorithm holds practical value, particularly for kinematic positioning applications requiring high accuracy and reliability.