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Updated: May 22, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Optimal satellite selection using quantum convolutional autoencoder for low-cost GNSS receiver applications.

Nalineekumari Arasavali1, Mogadala Vinod Kumar2, Sasibhushana Rao Gottapu3

  • 1Department of Electronics & Communication Engineering, Dadi Institute of Engineering & Technology(A), Visakhapatnam, Andhra Pradesh, India.

Scientific Reports
|March 14, 2025
PubMed
Summary

This study introduces a quantum convolutional autoencoder for optimal satellite selection in low-cost Global Navigation Satellite Systems (GNSS) receivers. The method significantly improves positioning accuracy and reduces computational load compared to traditional approaches.

Keywords:
Combined constellation, Sustainable Development Goal (SDG) 9:Industry, Innovation, and Infrastructure, SDG 11: Sustainable Cities and CommunitiesGNSSQuantum convolutional autoencoderSatellite selection

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Area of Science:

  • Satellite navigation systems
  • Quantum computing applications
  • Machine learning in optimization

Background:

  • Global Navigation Satellite Systems (GNSS) are essential, but low-cost receivers struggle with suboptimal performance due to high Geometric Dilution of Precision (GDOP).
  • Efficient satellite selection is critical for enhancing positioning accuracy and reliability in GNSS applications.

Purpose of the Study:

  • To develop an optimal satellite selection method for low-cost GNSS receivers using quantum computing and machine learning.
  • To minimize Geometric Dilution of Precision (GDOP) and optimize the tetrahedron volume function for improved positioning accuracy.

Main Methods:

  • Proposed a novel Quantum Convolutional Autoencoder (QCAE)-based optimal satellite selection method.
  • Utilized satellite data collected from a receiver at latitude 16.33°N and longitude 80.62°E on March 10, 2022.
  • Set Geometric Dilution of Precision (GDOP) as the cost function to identify optimal satellites for positioning.

Main Results:

  • The QCAE method achieved a Circular Error Probable (CEP) of 1.384 m and Spherical Error Probable (SEP) of 1.759 m for four satellites, outperforming PSOSSM (5.937 m CEP, 6.691 m SEP).
  • For nine satellites, QCAE yielded CEP of 1.287 m and SEP of 1.713 m, compared to PSOSSM's 5.725 m CEP and 6.385 m SEP.
  • QCAE reduced computations by over 64% (730 multiplications, 713 additions) compared to using all visible satellites (2034 multiplications, 2017 additions).

Conclusions:

  • The QCAE-based approach offers an optimal navigation solution for cost-effective, real-time GNSS implementations.
  • This research provides novel insights into satellite selection strategies leveraging advanced machine learning techniques.
  • The method enhances the performance of low-cost GNSS receivers by improving accuracy and computational efficiency.