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Enhanced range doppler mapping algorithm for passive GNSS based radar aerial target detection.

Zhuxian Zhang1, Ma Xiaojing2, Yu Zheng3

  • 1College of Electronic Communication and Electrical Engineering, Changsha University, Changsha, 410022, China.

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|November 25, 2025
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Summary

This study enhances Global Navigation Satellite System (GNSS) radar for detecting airborne targets by improving the signal-to-noise ratio (SNR) of Range Doppler Maps (RDM). The new method reduces noise based on signal characteristics, boosting detection accuracy.

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Area of Science:

  • Radar Systems
  • Signal Processing
  • Aerospace Engineering

Background:

  • Global Navigation Satellite System (GNSS) radar offers non-contact object detection.
  • Current limitations include weak signals and low resolution, restricting applications.
  • Detecting moving airborne targets presents unique challenges for GNSS radar.

Purpose of the Study:

  • To propose a novel Range Doppler Map (RDM) signal-to-noise ratio (SNR) improvement scheme for airborne target detection using GNSS radar.
  • To enhance the resolution and accuracy of target motion state estimation.
  • To develop a noise reduction algorithm applicable without prior environmental conditions.

Main Methods:

  • Employed a bi-static GNSS radar model to acquire RDMs from direct and reflected signals.
  • Developed an algorithm to successively deduct noise terms within GNSS signals.
  • Simulated moving target detection to obtain and analyze RDM spectra.

Main Results:

  • The proposed algorithm significantly improves RDM SNR by effectively reducing noise.
  • Noise reduction based on signal characteristics demonstrated greater applicability than environment-dependent methods.
  • Simulations showed reduced image information entropy post-noise reduction (63.23% for GPS, 71.69% for Beidou).

Conclusions:

  • The developed RDM SNR improvement algorithm enhances GNSS radar capabilities for airborne target detection.
  • The method offers improved resolution and accuracy in target motion analysis.
  • This signal-centric approach overcomes limitations of traditional noise reduction techniques.