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Data Acquisition Protocol for Determining Embedded Sensitivity Functions
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A Double Dwell High Sensitivity GPS Acquisition Scheme Using Binarized Convolution Neural Network.

Zhen Wang1, Yuan Zhuang2, Jun Yang3

  • 1National ASIC System Engineering Research Center, Southeast University, 2 Sipailou, Nanjing 210096, China. 230119228@seu.edu.cn.

Sensors (Basel, Switzerland)
|May 12, 2018
PubMed
Summary
This summary is machine-generated.

A new deep learning method using a binarized convolutional neural network (BCNN) significantly improves Global Positioning System (GPS) acquisition. This novel approach reduces computation by 80% and enhances signal detection by 2 dB compared to traditional methods.

Keywords:
GPS acquisitionbinarized convolution neural networkdouble dwellhigh sensitivity

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Satellite Navigation

Background:

  • Traditional Global Positioning System (GPS) acquisition methods like Max selection and threshold crossing (MAX/TC) rely on identifying correlation peaks.
  • These conventional techniques can be computationally intensive and may lack optimal sensitivity in challenging signal environments.

Purpose of the Study:

  • To introduce a novel deep learning approach for GPS acquisition using a multi-layer binarized convolutional neural network (BCNN).
  • To enhance the efficiency and performance of GPS signal acquisition by reducing computational overhead and improving detection accuracy.

Main Methods:

  • A double dwell acquisition strategy is employed, utilizing a short integration in the first dwell and a long integration in the second.
  • A BCNN is trained to recognize the GPS acquisition correlation envelope, identifying potential auto-correlation peaks.
  • The BCNN compresses the initial search space for acquisition parameters by a factor of 1023 in the first dwell.

Main Results:

  • The proposed BCNN-based method, termed double dwell/correlation envelope identification (DD/CEI), significantly reduces computational overhead to approximately 1/5 of conventional methods.
  • Experiments demonstrate a 2 dB improvement in acquisition performance compared to MAX/TC under identical specifications.
  • The compressed search space in the first dwell maintains low computational overhead despite the long integration in the second dwell.

Conclusions:

  • The BCNN-based DD/CEI method offers a more efficient and sensitive alternative for GPS acquisition.
  • This deep learning approach effectively reduces computational complexity while improving signal detection performance.
  • The findings suggest a promising direction for advancing satellite navigation signal processing techniques.