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Support Vector Regression for Mobile Target Localization in Indoor Environments.

Satish R Jondhale1, Vijay Mohan2, Bharat Bhushan Sharma3

  • 1Department of Electronics and Telecommunication, Amrutvahini College of Engineering, Sangamner 422608, Maharashtra, India.

Sensors (Basel, Switzerland)
|January 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces novel algorithms using support vector regression (SVR) for wireless sensor network (WSN) localization. These SVR-based methods improve indoor target localization accuracy compared to traditional techniques.

Keywords:
Kalman filter (KF)generalized regression neural network (GRNN)localization and tracking (L&T)received signal strength (RSS)support vector regression (SVR)trilaterationwireless sensor network (WSN)

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Received signal strength (RSS) based localization in wireless sensor networks (WSNs) suffers from inaccuracies in indoor environments due to signal fluctuations.
  • Improving localization accuracy is a significant research challenge for RSS-based systems.

Purpose of the Study:

  • To propose novel range-free localization algorithms using support vector regression (SVR) and SVR combined with a Kalman filter (KF).
  • To enhance indoor target localization accuracy in WSNs using RSS measurements.

Main Methods:

  • Development of two range-free algorithms: Support Vector Regression (SVR) and SVR + Kalman Filter (KF).
  • Directly estimating target locations from field measurements without distance computation, unlike trilateration.
  • Utilizing only three RSS measurements for localization, a reduction compared to other methods like GRNN.

Main Results:

  • The proposed SVR-based algorithms demonstrated superior indoor localization performance compared to trilateration and generalized regression neural network (GRNN) based schemes.
  • The fusion of SVR with KF further refined target location estimates.
  • Algorithms showed efficacy in noisy radio frequency (RF) channels and dynamic target motion models.

Conclusions:

  • SVR-based localization offers a more accurate and efficient approach for WSNs, particularly in challenging indoor environments.
  • The SVR + KF combination provides enhanced precision for target tracking.
  • The proposed methods represent a significant advancement over existing RSS-based localization techniques.