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Off-Grid DOA Estimation Based on Circularly Fully Convolutional Networks (CFCN) Using Space-Frequency

Wenqiong Zhang1,2, Yiwei Huang1,2, Jianfei Tong1

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Summary
This summary is machine-generated.

This study introduces an off-grid deep learning method for direction-of-arrival (DOA) estimation using circularly fully convolutional networks (CFCN). The CFCN-based approach enhances accuracy and resolution for micro-aperture arrays, overcoming limitations of existing models.

Keywords:
DOA estimationcircularly fully convolutional networkshigh resolutionoff-gridspace-frequency pseudo-spectrum

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Area of Science:

  • Signal Processing
  • Array Signal Processing
  • Machine Learning for Signal Processing

Background:

  • Direction-of-arrival (DOA) estimation using micro-aperture arrays is challenging, especially at low frequencies with multiple sources.
  • Existing deep learning (DL) methods often treat DOA estimation as a multi-label multi-classification problem, limiting accuracy due to grid dependency and dataset reliance.

Purpose of the Study:

  • To propose an novel off-grid deep learning (DL) based direction-of-arrival (DOA) estimation method.
  • To improve the accuracy, resolution, and generalization ability of DOA estimation for micro-aperture arrays.

Main Methods:

  • Developed an off-grid DL framework utilizing circularly fully convolutional networks (CFCN) as the backbone.
  • Employed space-frequency pseudo-spectra for training data labeling and generated on-grid DOA proposals.
  • Integrated a regressor to refine DOA estimates based on proposals and extracted spatial phase features via circular convolution.
  • Utilized rotating convolutional networks to enhance spatial resolution by increasing feature dimensionality.

Main Results:

  • The proposed CFCN-based method demonstrates superior performance compared to existing techniques.
  • Achieved significant improvements in generalization ability, resolution, and accuracy in both simulations and experimental validation.
  • The model effectively reduces network parameters while maintaining consistent interpretation ability across different sub-bands.

Conclusions:

  • The off-grid DL approach using CFCN offers a robust solution for low-frequency multi-source DOA estimation with micro-aperture arrays.
  • This method overcomes the limitations of grid-based DL models, providing more accurate and higher-resolution DOA estimations.
  • Experimental results confirm the practical applicability and effectiveness of the proposed CFCN-based DOA estimation framework.