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Diffusion Logarithm-Correntropy Algorithm for Parameter Estimation in Non-Stationary Environments over Sensor

Limei Hu1, Feng Chen2,3, Shukai Duan4

  • 1College of Electronic and Information Engineering, School of Mathematics and Statistics, Southwest University, Chongqing 400715, China. hlm0903@email.swu.edu.cn.

Sensors (Basel, Switzerland)
|October 13, 2018
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Summary
This summary is machine-generated.

This study introduces a new diffusion logarithm-correntropy algorithm for sensor networks facing changing environments. The algorithm improves parameter estimation accuracy by adapting to errors, leading to better tracking performance.

Keywords:
diffusion logarithm-correntropy algorithmnon-stationaryparameter estimationsensor networkstracking performance

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Area of Science:

  • Signal Processing
  • Wireless Sensor Networks
  • Adaptive Algorithms

Background:

  • Parameter estimation in sensor networks is challenging due to non-stationary environments where parameters change over time.
  • Existing algorithms may struggle to adapt effectively to these dynamic conditions, impacting estimation accuracy.
  • Accurate parameter tracking is crucial for reliable sensor network operation.

Purpose of the Study:

  • To propose a novel diffusion logarithm-correntropy algorithm for parameter estimation in non-stationary sensor networks.
  • To enhance the adaptability and accuracy of parameter estimation in dynamic environments.
  • To analyze the tracking performance of the proposed algorithm.

Main Methods:

  • Developed a diffusion logarithm-correntropy algorithm incorporating logarithm and correntropy criteria.
  • Designed the algorithm to adjust step size dynamically based on estimation error magnitude.
  • Analyzed the theoretical tracking performance of the proposed algorithm.

Main Results:

  • The proposed algorithm demonstrates improved parameter estimation performance in non-stationary environments.
  • The algorithm exhibits faster adaptation and smaller steady-state errors compared to existing methods.
  • Experimental results validate the effectiveness and superiority of the logarithm-correntropy algorithm.

Conclusions:

  • The diffusion logarithm-correntropy algorithm is a robust and effective solution for parameter estimation in non-stationary sensor networks.
  • The algorithm's adaptive nature allows for superior tracking performance and reduced estimation errors.
  • This work contributes a valuable tool for enhancing the reliability of sensor network applications.