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Low Complexity Beamspace Super Resolution for DOA Estimation of Linear Array.

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Summary
This summary is machine-generated.

This study introduces a low-dimensional beamspace atomic norm minimization (BS-ANM) for direction-of-arrival (DOA) estimation in radar and wireless systems. The novel approach significantly reduces computational complexity for large arrays, improving performance.

Keywords:
DOA estimationatomic norm minimizationbeamspacesemidefinite programming

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Area of Science:

  • Array Signal Processing
  • Wireless Communication
  • Radar Systems

Background:

  • Beamspace processing offers reduced complexity and improved performance in array signal processing.
  • Existing atomic norm minimization (ANM) methods for direction-of-arrival (DOA) estimation face high computational costs with large-scale arrays.

Purpose of the Study:

  • To develop a computationally efficient beamspace atomic norm minimization (BS-ANM) approach for DOA estimation.
  • To address the limitations of high computational complexity in current ANM-based DOA estimation techniques for large arrays.

Main Methods:

  • A low-dimensional semidefinite programming (SDP) implementation of BS-ANM is proposed for DFT beamspace.
  • An efficient iterative algorithm based on the alternating direction method of multipliers (ADMM) is developed.
  • Covariance-based DOA estimation methods utilizing BS-ANM are introduced.

Main Results:

  • The proposed BS-ANM approach offers a significant reduction in computational complexity compared to traditional methods.
  • The developed ADMM-based algorithm provides efficient iterative solutions for DOA estimation.
  • Application of BS-ANM to channel estimation in massive MIMO systems demonstrates its effectiveness.

Conclusions:

  • The proposed BS-ANM methods achieve superior performance in DOA estimation for linear arrays.
  • The computational efficiency and performance improvements are validated through simulations against state-of-the-art methods.