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Updated: Jul 27, 2025

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Drone Controller Localization Based on RSSI Ratio.

Yuhong Wang1, Yonghong Zeng1, Sumei Sun1

  • 1Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
Summary
This summary is machine-generated.

We developed two drone controller localization methods using received signal strength indicator (RSSI) ratios. These methods outperform existing algorithms in simulations and field trials, enhancing drone tracking accuracy.

Keywords:
RSSI fingerprintWLAN channellocalizationtwo-ray ground reflection

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Area of Science:

  • Robotics and Control Systems
  • Wireless Communication and Networking
  • Signal Processing

Background:

  • Accurate localization of drone controllers is crucial for safe and efficient operation.
  • Existing localization methods may face challenges in complex wireless environments.
  • Received Signal Strength Indicator (RSSI) ratios offer a potential avenue for localization.

Purpose of the Study:

  • To propose and evaluate novel methods for drone controller localization using RSSI ratios.
  • To compare the performance of proposed methods against existing algorithms.
  • To investigate the impact of environmental factors and system parameters on localization accuracy.

Main Methods:

  • Development of two RSSI ratio-based localization algorithms: fingerprinting and model-based.
  • Conducting extensive simulations in various channel conditions (WLAN, TRGR).
  • Performing real-world field trials to validate simulation findings.

Main Results:

  • Proposed RSSI ratio methods demonstrated superior performance compared to the distance mapping algorithm in WLAN channels.
  • Increased sensor count and RSSI ratio sample averaging improved localization accuracy, particularly in non-fading channels.
  • Grid size reduction enhanced performance in low shadowing scenarios, with diminishing returns in high shadowing.

Conclusions:

  • The proposed RSSI ratio fingerprint and model-based algorithms offer a robust and effective solution for drone controller localization.
  • Localization accuracy is influenced by the number of sensors, signal averaging, and channel characteristics.
  • Field trial results corroborate simulation findings, confirming the practical applicability of the methods.