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RSS-Based Method for Sensor Localization with Unknown Transmit Power and Uncertainty in Path Loss Exponent.

Jiyan Huang1,2, Peng Liu3, Wei Lin4

  • 1School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China. huangjiyan@uestc.edu.cn.

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|September 13, 2016
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Summary
This summary is machine-generated.

This study introduces a new method for sensor localization in wireless sensor networks (WSNs) that provides a direct solution without complex searching. The novel approach accurately estimates sensor locations even with unknown transmit power and path loss exponent (PLE).

Keywords:
cramer-rao lower bound (CRLB)path loss exponent (PLE)received signal strength (RSS)sensor localizationtransmit power

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Area of Science:

  • Wireless Sensor Networks (WSNs)
  • Localization Algorithms
  • Signal Processing

Background:

  • Sensor localization is crucial for Wireless Sensor Networks (WSNs).
  • Received Signal Strength (RSS) localization is a common technique.
  • Existing RSS methods struggle with unknown transmit power and path loss exponent (PLE), lacking closed-form solutions.

Purpose of the Study:

  • To develop a novel Received Signal Strength (RSS) localization method for WSNs.
  • To provide a closed-form solution for sensor localization.
  • To address scenarios with unknown transmit power and uncertain path loss exponent (PLE).

Main Methods:

  • A two-step weighted least squares estimator is proposed.
  • The method derives theoretical variance and Cramer-Rao lower bound (CRLB).
  • Analysis includes relationships between deterministic and stochastic CRLB.

Main Results:

  • A novel RSS localization method with a closed-form solution is presented.
  • The method effectively handles unknown transmit power and uncertain PLE.
  • The proposed method is proven to achieve the stochastic Cramer-Rao lower bound (CRLB).

Conclusions:

  • The novel two-step weighted least squares estimator offers an efficient closed-form solution for WSN localization.
  • This method overcomes limitations of existing RSS-based techniques by addressing unknown parameters.
  • The proposed localization approach demonstrates optimal performance, reaching the stochastic CRLB.