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A-CRNN-Based Method for Coherent DOA Estimation with Unknown Source Number.

Yuanyuan Yao1,2, Hong Lei2, Wenjing He1,2

  • 1Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China.

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|April 23, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for direction of arrival (DOA) estimation, overcoming limitations of existing deep learning methods for coherent signals. The approach enhances accuracy and robustness, even with unknown source numbers and imperfect conditions.

Keywords:
Toeplitz matrix reconstructioncolored Gaussian noise, coherent sourcesconvolutional-recurrent neural networkdirection-of-arrival (DOA) estimation

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Array Signal Processing

Background:

  • Direction of Arrival (DOA) estimation is challenging without knowing the number of sources, especially with coherent signals.
  • Existing deep learning (DL) methods struggle with variable source numbers and require signal independence.

Purpose of the Study:

  • To propose a new framework for DOA estimation that handles unknown source numbers and coherent signals.
  • To improve the accuracy and robustness of DOA estimation in practical scenarios.

Main Methods:

  • A novel framework combining parallel DOA estimators with Toeplitz matrix reconstruction.
  • Each estimator uses a spatial filter based on convolutional-recurrent neural networks and a multi-label classifier.
  • Toeplitz-based source number determination assists in reducing estimation errors.

Main Results:

  • The proposed method is data-driven and inherently immune to signal coherence.
  • Simulation results demonstrate superior performance compared to existing methods.
  • The model shows robustness against limited snapshots, colored noise, and array imperfections.

Conclusions:

  • The new framework effectively addresses DOA estimation challenges with coherent sources and unknown numbers.
  • The method offers a robust and accurate solution for complex signal environments.
  • This approach advances the field of array signal processing and machine learning applications.