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An Overlapping-Signal Separation Algorithm Based on a Self-Attention Neural Network for Space-Based ADS-B.

Ziwei Liu1, Shuyi Tang1, Yehua Cao1

  • 1School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

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Summary
This summary is machine-generated.

New deep learning models, SplitNet-2 and SplitNet-3, effectively address signal collisions in space-based automatic dependent surveillance-broadcast (ADS-B) systems. These advanced models improve aircraft surveillance reliability and global coverage by enhancing signal separation.

Keywords:
ADS-Bblind source separationsatellite communicationsself-attentionsignal separation

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Area of Science:

  • Aerospace Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Space-based automatic dependent surveillance-broadcast (ADS-B) systems promise global aircraft surveillance.
  • Severe signal collisions from multiple asynchronous transmissions pose a significant challenge in satellite reception.
  • Existing collision mitigation techniques are computationally intensive or require specific hardware, limiting satellite applicability.

Purpose of the Study:

  • To develop novel deep learning models for mitigating signal collisions in space-based ADS-B systems.
  • To overcome the limitations of traditional signal separation methods in satellite scenarios.
  • To enhance the reliability and coverage of satellite-based ADS-B surveillance.

Main Methods:

  • Proposed SplitNet-2: a Transformer-inspired self-attention model for separating two overlapping ADS-B signals.
  • Proposed SplitNet-3: a convolutional residual U-shaped network for disentangling three simultaneous ADS-B signals.
  • Conducted extensive simulations under realistic satellite reception conditions.

Main Results:

  • SplitNet-2 and SplitNet-3 significantly outperform conventional methods in signal separation.
  • Achieved lower bit error rates (BERs) compared to existing techniques.
  • Demonstrated improved demodulation accuracy for colliding ADS-B signals.

Conclusions:

  • The proposed deep learning models offer a practical solution for underdetermined signal separation in space-based ADS-B reception.
  • SplitNet-2 and SplitNet-3 enhance the reliability and expand the coverage of satellite-based ADS-B surveillance.
  • These advancements pave the way for more robust global aircraft monitoring systems.