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An Indoor Robust Localization Algorithm Based on Data Association Technique.

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  • 1Department of Computer and Communication Engineering, Northeastern University, Qinhuangdao 066004, China.

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|November 21, 2020
PubMed
Summary
This summary is machine-generated.

This study addresses Non-Line-of-Sight (NLOS) errors in wireless sensor network (WSN) indoor localization. The proposed method improves positioning accuracy and robustness by identifying and mitigating NLOS signal transmissions.

Keywords:
data association techniquelocalizationnon-line-of-sightrobustness

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Area of Science:

  • * Computer Science
  • * Electrical Engineering
  • * Signal Processing

Background:

  • * Wireless Sensor Networks (WSNs) are crucial for Internet of Things (IoT) applications, particularly indoor localization.
  • * Non-Line-of-Sight (NLOS) signal propagation significantly degrades the accuracy of WSN-based indoor positioning systems.
  • * Mitigating NLOS errors is a critical challenge for reliable indoor localization.

Purpose of the Study:

  • * To develop a robust algorithm for accurate indoor localization in the presence of NLOS propagation.
  • * To enhance the reliability of WSN positioning by effectively identifying and handling NLOS signals.
  • * To improve the overall performance of indoor localization systems.

Main Methods:

  • * Grouping anchor nodes and employing Maximum Likelihood Estimation (MLE) for initial position estimation.
  • * Implementing an NLOS identification method to discard position estimates corrupted by NLOS transmission.
  • * Utilizing hypothesis testing and a poly-probability matrix to calculate LOS/NLOS probabilities and refine target position.
  • * Incorporating an NLOS tracking method to maintain accuracy during intermittent position estimation.

Main Results:

  • * The proposed algorithm effectively identifies and mitigates NLOS transmission effects.
  • * Position estimates polluted by NLOS signals were successfully discarded.
  • * Calculated probabilities of LOS and NLOS transmissions refined target positioning.
  • * The NLOS tracking method prevented positioning errors when continuous estimation was unavailable.

Conclusions:

  • * The developed algorithm demonstrates superior positioning accuracy compared to existing methods.
  • * The proposed approach exhibits enhanced robustness in challenging indoor environments.
  • * This work offers a significant advancement in overcoming NLOS challenges for WSN indoor localization.