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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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Published on: January 18, 2020

Differential Iterative Joint Estimation Approach for Indoor Target Localization.

Zhigang Su1, Jingyuan Xu1, Jingtang Hao1

  • 1Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin 300300, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a Differential Iterative Joint Estimation (DIJE) method to improve indoor localization accuracy using received signal strength indication (RSSI). DIJE enhances robustness against signal variations and parameter mismatches for reliable positioning.

Keywords:
Differential Iterative Joint Estimationeigenvalue methodindoor localizationpath-loss exponentreceived signal strength indicator

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Area of Science:

  • Wireless communication and localization technologies.
  • Signal processing and estimation theory.
  • Indoor positioning systems.

Background:

  • Received signal strength indication (RSSI)-based indoor localization methods suffer from degraded accuracy and robustness due to reference RSSI and path-loss exponent mismatches.
  • Existing methods often fail to adapt to environmental variations and parameter uncertainties.

Purpose of the Study:

  • To propose a novel Differential Iterative Joint Estimation (DIJE) localization method to overcome the limitations of traditional RSSI-based methods.
  • To enhance the accuracy, robustness, and adaptability of indoor localization systems.

Main Methods:

  • Employs a differential model to eliminate reference RSSI uncertainty, converting the maximum likelihood estimation (MLE) problem into a matrix eigenvalue problem.
  • Utilizes an alternating iterative optimization framework for joint estimation of target position and path-loss exponent.
  • Derives the Cramér-Rao Lower Bound (CRLB) for dual-parameter uncertainty as a theoretical benchmark.

Main Results:

  • The DIJE method demonstrates high accuracy and fast target position estimation.
  • Achieves adaptive joint estimation, effectively suppressing performance degradation caused by parameter mismatches.
  • Performance approaches the theoretical CRLB limit across various noise levels, access point densities, and complex indoor environments.

Conclusions:

  • The DIJE method significantly outperforms classical algorithms (RSDPE, MLE-TLLS, SOCP3, LCJE) in localization accuracy, robustness, and adaptability.
  • Meets engineering requirements for high-accuracy, low-latency, real-time indoor localization.