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<p>In recent years, sparse array design for underdetermined direction of arrival (DOA) estimation has made significant progress, and various improved structures have been proposed to expand the virtual aperture and enhance estimation accuracy. However, few studies have successfully combined a large array aperture and high degrees of freedom (DOFs) with the number of array elements. Inspired by the maximum inter-element spacing constraint (IES) criterion, this paper proposes four novel generalized extended nested arrays (GENAs). Each GENA consists of five uniform linear subarrays (ULAs) and a properly placed independent sensor. For any given number of sensors, closed-form expressions for sensor positions, achievable DOFs and weight functions are derived in detail for each GENA. It is proven that the designs of these four arrays have a hole-free difference co-array. Compared with the existing sparse array structures, the GENA can provide a higher number of uniform DOFs, as well as relatively small mutual coupling effects. Numerical simulations demonstrate that the proposed GENA exhibits significantly enhanced DOA estimation performance under various SNR and snapshot conditions.</p>

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Generalized Extended Nested Array for Higher uDOFs Based on Maximum Inter-Element Spacing Principle

  • 期刊论文

  • 发表日期：

    2026年2月7日

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  • Guojun Jiang,
  • Meng Li,
  • Yunlong Yang

摘要

In recent years, sparse array design for underdetermined direction of arrival (DOA) estimation has made significant progress, and various improved structures have been proposed to expand the virtual aperture and enhance estimation accuracy. However, few studies have successfully combined a large array aperture and high degrees of freedom (DOFs) with the number of array elements. Inspired by the maximum inter-element spacing constraint (IES) criterion, this paper proposes four novel generalized extended nested arrays (GENAs). Each GENA consists of five uniform linear subarrays (ULAs) and a properly placed independent sensor. For any given number of sensors, closed-form expressions for sensor positions, achievable DOFs and weight functions are derived in detail for each GENA. It is proven that the designs of these four arrays have a hole-free difference co-array. Compared with the existing sparse array structures, the GENA can provide a higher number of uniform DOFs, as well as relatively small mutual coupling effects. Numerical simulations demonstrate that the proposed GENA exhibits significantly enhanced DOA estimation performance under various SNR and snapshot conditions.