Sparse Linear Antenna Arrays: A Review

Abstract

Linear sparse antenna arrays have been widely studied in array processing literature. They belong to the general class of non-uniform linear arrays (NULAs). Sparse arrays need fewer sensor elements than uniform linear arrays (ULAs) to realize a given aperture. Alternately, for a given number of sensors, sparse arrays provide larger apertures and higher degrees of freedom than full arrays (ability to detect more source signals through direction-of-arrival (DOA) estimation). Another advantage of sparse arrays is that they are less affected by mutual coupling compared to ULAs. Different types of linear sparse arrays have been studied in the past. While minimum redundancy arrays (MRAs) and minimum hole arrays (MHAs) existed for more than five decades, other sparse arrays such as nested arrays, co-prime arrays and super-nested arrays have been introduced in the past decade. Subsequent to the introduction of co-prime and nested arrays in the past decade, many modifications, improvements and alternate sensor array configurations have been presented in the literature in the past five years (2015–2020). The use of sparse arrays in future communication systems is promising as they operate with little or no degradation in performance compared to ULAs. In this chapter, various linear sparse arrays have been compared with respect to parameters such as the aperture provided for a given number of sensors, ability to provide large hole-free co-arrays, higher degrees of freedom (DOFs), sharp angular resolutions and susceptibility to mutual coupling. The chapter concludes with a few recommendations and possible future research directions.

Direction-of-Arrival Estimation via Sparse Bayesian Learning Exploiting Hierarchical Priors with Low Complexity

“…In recent years, relevant studies on DOA estimation mainly focus on sparse array configuration [15][16][17] or sparsity-based method innovation [18,19]. For the latter, three categories can be summarized out of massive documents.…”

Augmented improved nested array with enhanced degrees of freedom and reduced mutual coupling effect

“…Many nested‐like arrays have been presented in the past decade [8], including CPA with compressed inter‐element spacing (CACIS), coprime array with displaced subarrays (CADiS) [5], generalized nested array (GNA) [6], super nested array (SNA) [4], augmented nested array (ANA) [14] etc. Nevertheless, CACIS, CADiS, and ANA are difficult to achieve balance between high uniform DOFs (uDOFs) and low mutual coupling [13].…”

“…Therefore, the sparse arrays have the ability to detect more source signals through DOA estimation. Another advantage of the sparse arrays is that they are less affected by mutual coupling compared to ULAs [7,8].…”

GSNA: A Novel Sparse Array Design Achieving Enhanced Degree of Freedom for Noncircular Sources

“…The traditional uniform linear array (ULA) with N sensors can only resolve N − 1 individual signals, and it rarely meets the demands of the currently complex electromagnetic environment. By contrast, the layouts of sparse arrays [1] break through the limitation of the Nyquist sampling theorem and obtain signal information in a broader spatial range, thus achieving enhanced DOF, weaker mutual coupling, and higher resolution.…”