Statistically designed density-tapered arrays

Skolnik, M. I.; Sherman, John W.; Ogg, Frank C.

doi:10.1109/tap.1964.1138239

Cited by 158 publications

(99 citation statements)

References 18 publications

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“…The promising features of the sparse arrays (first reported about half a century ago [9]) have led to an intense research efforts in this field. Originally, synthesis strategies based on the iterative least squares [10], steepest-descent technique [11], and optimum autocorrelation function [12] were proposed.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

Compressive Sensing Multiplicative Antenna Array

Abbasi

Fusco

Zelenchuk

2018

IEEE Trans. Antennas Propagat.

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“…In the same year, this area was further refined with the evolution of "thinned array antenna". It is the method of varying the geometry of an array to reach the desired performance [4,5]. To niche down the computational cost, a new approach based on Gauss-quadrature was proposed [6].…”

Section: Introductionmentioning

confidence: 99%

Directivity Gain Improvement in Phase Array Antenna via a Novel L2-Norm Adaptive Beamforming Algorithm

Jafarian¹,

Zubair

Rizvi

et al. 2017

IJFGCN

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“…Since no deterministic method can be found for array thinning in such large arrays, probabilistic methods have been attempted. These focus on density of "on" elements in different parts of array and its effects on far-field pattern [6]. In recent years, nongradient-based optimization methods such as genetic algorithms, ant colony technique and particle swarm optimization [7][8][9][10] have been found useful in solving such problems.…”

Section: Introductionmentioning

confidence: 99%

Solving “Antenna Array Thinning Problem” Using Genetic Algorithm

Jain

Mani²

2012

Applied Computational Intelligence and Soft Computing

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Thinning involves reducing total number of active elements in an antenna array without causing major degradation in system performance. Dynamic thinning is the process of achieving this under real-time conditions. It is required to find a strategic subset of antenna elements for thinning so as to have its optimum performance. From a mathematical perspective this is a nonlinear, multidimensional problem with multiple objectives and many constraints. Solution for such problem cannot be obtained by classical analytical techniques. It will be required to employ some type of search algorithm which can lead to a practical solution in an optimal. The present paper discusses an approach of using genetic algorithm for array thinning. After discussing the basic concept involving antenna array, array thinning, dynamic thinning, and application methodology, simulation results of applying the technique to linear and planar arrays are presented.

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Statistically designed density-tapered arrays

Cited by 158 publications

References 18 publications

Compressive Sensing Multiplicative Antenna Array

Compressive Sensing Multiplicative Antenna Array

Directivity Gain Improvement in Phase Array Antenna via a Novel L2-Norm Adaptive Beamforming Algorithm

Solving “Antenna Array Thinning Problem” Using Genetic Algorithm

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