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Computationally Efficient Direction-of-Arrival Estimation Algorithms for a Cubic Coprime Array.

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  • 1College of Electronic Engineering, Nanjing Vocational University of Industry Technology, Nanjing 211106, China.

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Summary
This summary is machine-generated.

This study introduces novel algorithms for direction-of-arrival (DOA) estimation in massive MIMO radar using coprime cubic arrays. These methods enhance accuracy and reduce computational complexity for improved radar performance.

Keywords:
Cramer–Rao boundambiguity eliminationdirection-of-arrival estimationmassive multi-input multi-outputsuccessive algorithmtotal array-based multiple signal classification

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing
  • Array Signal Processing

Background:

  • Massive MIMO radar systems require accurate direction-of-arrival (DOA) estimation for enhanced spatial resolution and target tracking.
  • Conventional DOA estimation algorithms face challenges with high complexity and potential phase ambiguities in complex array configurations.

Purpose of the Study:

  • To propose and evaluate new DOA estimation algorithms for massive MIMO radar utilizing coprime cubic arrays (CCA).
  • To address limitations of existing methods, specifically phase ambiguity and computational load, in 2D DOA estimation.

Main Methods:

  • Development of a Total Array-based Multiple Signals Classification (TA-MUSIC) algorithm leveraging auto- and mutual covariance matrices for full degrees of freedom (DOFs) utilization.
  • Introduction of an Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT)-based MUSIC (E-MUSIC) algorithm for computational efficiency.
  • Verification of phase ambiguity elimination through the coprime property of the CCA.

Main Results:

  • The TA-MUSIC algorithm effectively utilizes full DOFs and resolves phase ambiguities inherent in conventional MUSIC algorithms.
  • The E-MUSIC algorithm demonstrates significant computational efficiency while maintaining accurate DOA estimation.
  • Numerical simulations confirm the superior performance and effectiveness of the proposed TA-MUSIC and E-MUSIC algorithms compared to benchmarks.

Conclusions:

  • The proposed TA-MUSIC and E-MUSIC algorithms offer advanced solutions for 2D DOA estimation in massive MIMO radar with CCAs.
  • These algorithms provide a robust and computationally efficient approach, overcoming limitations of existing methods.
  • The findings contribute to the advancement of radar signal processing for improved surveillance and tracking capabilities.