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Sparse array angle estimation using reduced-dimension ESPRIT-MUSIC in MIMO radar.

Chaozhu Zhang1, Yucai Pang1

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This study introduces a new method for bistatic MIMO radar using sparse linear arrays to solve manifold ambiguity in angle estimation. The Reduced-Dimension ESPRIT-MUSIC algorithm effectively reduces computational complexity while accurately estimating angles.

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing
  • Array Signal Processing

Background:

  • Sparse linear arrays offer advantages over filled arrays in radar systems, including improved angle estimation, resolution, reduced size, and lower cost.
  • Bistatic multiple-input multiple-output (MIMO) radar systems utilizing sparse linear arrays face challenges with manifold ambiguity, complicating accurate angle estimation.

Purpose of the Study:

  • To address the manifold ambiguity issue in bistatic MIMO radar with sparse linear arrays.
  • To develop an efficient algorithm for accurate paired transmit and receive angle estimation.

Main Methods:

  • An ESPRIT-MUSIC method was developed, employing the ESPRIT algorithm for initial ambiguous angle estimates and a MUSIC-based procedure for disambiguation.
  • A Reduced-Dimension ESPRIT-MUSIC (RD-ESPRIT-MUSIC) algorithm was proposed to decrease computational complexity by reducing the search dimension.

Main Results:

  • The ESPRIT-MUSIC method successfully estimates paired transmit and receive angles, resolving manifold ambiguity.
  • The RD-ESPRIT-MUSIC algorithm significantly reduces computational complexity, requiring only a one-dimension search, while maintaining effectiveness.

Conclusions:

  • The proposed RD-ESPRIT-MUSIC algorithm effectively solves the manifold ambiguity problem in bistatic MIMO radar with sparse linear arrays.
  • The method offers a computationally efficient solution for accurate angle estimation in such radar systems.