Wide-Angle Beam Scanning Phased Array Antennas: A Review

Li, Ming; Chen, Shu‐Lin; Liu, Yanhui; Guo, Y. Jay

doi:10.1109/ojap.2023.3296636

Cited by 13 publications

(2 citation statements)

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“…Because this pattern is not symmetric about the xy-plane, the full space is shown when plotting the array patterns. The region of sidelobes is [9,180] • × [0, 360] • , which is sampled by δ θ = 2 • and δ ϕ = 4 • . The parameters α m and β m are set to 0 and 1.8/M, respectively.…”

Section: Experiments 2: Uha With Rotational Symmetrymentioning

confidence: 99%

“…They have been successfully applied to various fields such as radar [1], wireless communication [2], satellite navigation [3], radio astronomy [4], speech enhancement [5], and seismic exploration [6]. Array patterns with low sidelobes are very important in these applications to cope with unwanted interferences [7][8][9]. One of the most widely used methods to synthesize patterns with low sidelobes is amplitude weighting (also called amplitude taper).…”

Section: Introductionmentioning

confidence: 99%

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Fast Low-Sidelobe Pattern Synthesis Using the Symmetry of Array Geometry

Zhang,

Liu,

Zhou

et al. 2024

Sensors

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Array pattern synthesis with low sidelobe levels is widely used in practice. An effective way to incorporate sensor patterns in the design procedure is to use numerical optimization methods. However, the dimension of the optimization variables is very high for large-scale arrays, leading to high computational complexity. Fortunately, sensor arrays used in practice usually have symmetric structures that can be utilized to accelerate the optimization algorithms. This paper studies a fast pattern synthesis method by using the symmetry of array geometry. In this method, the problem of amplitude weighting is formulated as a second-order cone programming (SOCP) problem, in which the dynamic range of the weighting coefficients can also be taken into account. Then, by utilizing the symmetric property of array geometry, the dimension of the optimization problem as well as the number of constraints can be reduced significantly. As a consequence, the computational efficiency is greatly improved. Numerical experiments show that, for a uniform rectangular array (URA) with 1024 sensors, the computational efficiency is improved by a factor of 158, while for a uniform hexagonal array (UHA) with 1261 sensors, the improvement factor is 284.

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Section: Experiments 2: Uha With Rotational Symmetrymentioning

confidence: 99%