Maximally Sparse, Steerable, and Nonsuperdirective Array Antennas via Convex Optimizations

D’Urso, M.; Prisco, G.; Tumolo, Roberto Michele

doi:10.1109/tap.2016.2586490

Cited by 54 publications

(40 citation statements)

References 27 publications

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“…[26], [34], [22]). In contrast to these approaches, in this paper we use a multiplicative array lattice with multi-beam directional modulation implementation (Fig.…”

Section: Numerical Example and Resultsmentioning

confidence: 99%

“…5) with other rectangular or non-rectangular 2-D planar array lattices. For example, the formulation in [22] (equation (6)) shows an array thinning from 41 to 35 while attempting to match -17.5…”

Section: Numerical Example and Resultsmentioning

confidence: 99%

“…Recent research efforts have shown to outperform previous approaches in terms of required radiation characteristics and convergence speed. Some of the most notable techniques include genetic algorithm (GA) [16], [17], iterative Fourier transform (IFT) [10], [18], particle swarm (PS) [19]- [21], multi-level branch-and-bound (B&B) [22] matrix pencil method [23], Compressive sensing [24], [25], invasive weed (IW) [26], ant colony [27] and so on. A number of hybrid schemes of multiple approaches were also proposed [28], [29].…”

Section: Introductionmentioning

confidence: 99%

“…Next we propose a multi-beam orthogonal beam space synthesis method, based on a directional modulation approach, [32], [33], and demonstrate an example three beam solution. In contrast to dynamically steerable beam satisfying a priori reference field pattern [22], [26], [34], [35] the proposed approach focus only on the fixed beam operation which requires minimum hardware cost. Theoretical formulation of the approach is presented in section II, implementation of the approach with an aid of a numerical example and corresponding results are discussed in section III, while the findings are concluded in section IV of the paper.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Compressive Sensing Multiplicative Antenna Array

Abbasi

Fusco

Zelenchuk

2018

IEEE Trans. Antennas Propagat.

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Abstract-This paper presents a novel approach to formulate an aggressively thinned sparse antenna array suitable for orthogonal multi-beam receiver applications. The power patterns of M×N element planar rectangular array are first reduced to orthogonally placed cross multiplicative sub-arrays. These arrays are then re-distributed using a compressive sensing (CS) approach in order to achieve array thinning along two 1-D sub-arrays for a fixed steered beam projection. A multi-beam synthesis approach is then implemented which permits efficient beam maxima as well as null placement of multiple interlaced far-field patterns. Numerical examples are presented to show the implementation of the proposed approach.

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“…[26], [34], [22]). In contrast to these approaches, in this paper we use a multiplicative array lattice with multi-beam directional modulation implementation (Fig.…”

Section: Numerical Example and Resultsmentioning

confidence: 99%

Section: Numerical Example and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Compressive Sensing Multiplicative Antenna Array

Abbasi

Fusco

Zelenchuk

2018

IEEE Trans. Antennas Propagat.

View full text Add to dashboard Cite

show abstract

“…The aforementioned classification is obviously a very simplified one, since the years hybrid and mixed strategies have been presented [35][36][37][38][39][40][41]; furthermore, the recent development of compressive sensing [42], and related algorithms, has provided effective and computationally-efficient algorithms [43][44][45][46][47][48][49][50][51][52][53]. In particular, the synthesis of sparse linear arrays of hundreds of wavelengths was shown in [53], a result that to the best knowledge of the authors is currently unmatched.…”

Section: Introductionmentioning

confidence: 99%

Efficient Large Sparse Arrays Synthesis by Means of Smooth Re-Weighted L1 Minimization

et al. 2019

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In this paper, we present an efficient technique for the synthesis of very large sparse arrays, with arbitrary circularly-symmetrical upper bounds for the pattern specifications. The algorithm, which is based on iterative smooth re-weighted L1 minimizations, is very flexible and is capable of achieving very good performances with respect to competitive algorithms. Furthermore, thanks to its efficiency, planar arrays of hundreds of wavelengths can be synthesized with limited computational effort.

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Meta‐heuristic optimization algorithms for placement of multiple nulls and minimization of side lobe level in quadrant symmetric sparse array antenna

Dutta

Misra

Mahanti

2022

Int J Communication

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Summary The optimization of the non‐uniform planar array antenna parameter in order to minimize the signal interference in the electromagnetic environment through null placement is one of the most important areas of investigation in the field of wireless communication. This study deals with an effective method of synthesis of a quadrant symmetric sparse planar array antenna using the novel implementation of the well‐known meta‐heuristic optimization algorithms, namely, symbiotic organism search (SOS) and moth flame optimization (MFO) algorithms. The objective of this work is to place multiple small and wide nulls at the desired angles and simultaneously minimize the side lobe levels using a nonlinear mathematical approach. Three examples are presented with constraints that include the inter‐elemental distance, fixed number of array elements in the aperture, and the aperture size. The radiating antenna array elements are considered to have fixed uniform current distribution. The meta‐heuristic optimization approach offers the advantages of avoiding the infeasible solution while finding the best optimal set of element positions to reduce side lobe level in the desired radiation pattern. The radiation pattern characteristics and the computation time associated with each example and each algorithm are recorded and compared for arriving at the conclusion. The superiority of SOS is found in terms of the optimized side lobe levels. At the same time, both algorithms are found to be efficient in positioning the symmetric nulls in desired locations in the radiation pattern successfully. When compared with the published papers, proposed approach yields better results confirming the effectiveness of the performance of these algorithms.

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Maximally Sparse, Steerable, and Nonsuperdirective Array Antennas via Convex Optimizations

Cited by 54 publications

References 27 publications

Compressive Sensing Multiplicative Antenna Array

Compressive Sensing Multiplicative Antenna Array

Efficient Large Sparse Arrays Synthesis by Means of Smooth Re-Weighted L1 Minimization

Meta‐heuristic optimization algorithms for placement of multiple nulls and minimization of side lobe level in quadrant symmetric sparse array antenna

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