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<p>This paper presents a novel method for handling correlated and uncorrelated signals using three-dimensional (3D) sparse arrays. Conventional coarray techniques encounter limitations in such scenarios. Exploiting compressed sensing principles, our approach integrates the advanced Compressive Orthogonal Matching Pursuit with Enhanced Sparsity (COMP-ESP) algorithm. Beginning with a refined covariance matrix, we establish a robust sparse representation framework using a sophisticated 3D redundant dictionary in tandem with COMP-ESP. This enables precise extraction of sparse vectors, revealing accurate off-grid source directions for both correlated and uncorrelated signals. Our method overcomes traditional sparse array constraints, efficiently utilizing vectorized data to estimate more signals than the available sensors permit. Extensive empirical validation confirms the superiority and effectiveness of our approach in 3D sparse array scenarios, highlighting the transformative capabilities of COMP-ESP.</p>

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3D sparse sensor array for coherent and uncorrelated signals DOA estimation using novel COMP-ESP

  • 期刊论文

  • 发表日期：

    2025年2月13日

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  • Veerendra Dakulagi,
  • Astha Sharma,
  • Megharani Patil,
  • Miguel Villagómez Galindo,
  • Ana Beatriz Martinez Valencia,
  • Kannan Nova

摘要

This paper presents a novel method for handling correlated and uncorrelated signals using three-dimensional (3D) sparse arrays. Conventional coarray techniques encounter limitations in such scenarios. Exploiting compressed sensing principles, our approach integrates the advanced Compressive Orthogonal Matching Pursuit with Enhanced Sparsity (COMP-ESP) algorithm. Beginning with a refined covariance matrix, we establish a robust sparse representation framework using a sophisticated 3D redundant dictionary in tandem with COMP-ESP. This enables precise extraction of sparse vectors, revealing accurate off-grid source directions for both correlated and uncorrelated signals. Our method overcomes traditional sparse array constraints, efficiently utilizing vectorized data to estimate more signals than the available sensors permit. Extensive empirical validation confirms the superiority and effectiveness of our approach in 3D sparse array scenarios, highlighting the transformative capabilities of COMP-ESP.