Reducing the Number of Elements in Linear and Planar Antenna Arrays With Sparseness Constrained Optimization

Zhang, Wenji; Lian, Lulu; Li, Fang

doi:10.1109/tap.2011.2158943

Cited by 94 publications

(61 citation statements)

References 13 publications

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“…However, the use of a not redundant number of elements is generally advantageous, since it allows to reduce the feeding network complexity and the antenna weight. For this reason, in the last years, several efforts have been made to propose techniques for the design of arrays, linear and planar, with a reduced number of elements not equally spaced [23][24][25][26]. Due to their probabilistic nature, BOA and M-BOA seem particularly suitable to determine, during the optimization process, also the proper number of array elements, and therefore the design of such kind of array seems a particularly suitable test case for comparing their performances.…”

Section: M-boa Based Linear Array Synthesismentioning

confidence: 99%

“…All these optimization approaches generally allow to obtain optimum solution using as free parameters the position and/or the excitation of the array elements, while their number is fixed "a priori". Recently, some published papers [23][24][25][26] proved the possibility to fulfill the design constraints while reducing the number of elements in non-uniformly spaced arrays. In [23,24] the number of elements in the array is achieved by the singular value decomposition (SDV) approach, while the proper excitation and location of the elements is obtained with the matrix pencil method (MPM); in [25] sparseness constrained optimization is adopted, and finally in [26] the "probability" of different values of the number of array elements is provided, by sampling the distribution using Bayesian Interference (BI); this method, however, has to take into account the information provided by thousands of samples in order to get the proper probability distribution.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, some published papers [23][24][25][26] proved the possibility to fulfill the design constraints while reducing the number of elements in non-uniformly spaced arrays. In [23,24] the number of elements in the array is achieved by the singular value decomposition (SDV) approach, while the proper excitation and location of the elements is obtained with the matrix pencil method (MPM); in [25] sparseness constrained optimization is adopted, and finally in [26] the "probability" of different values of the number of array elements is provided, by sampling the distribution using Bayesian Interference (BI); this method, however, has to take into account the information provided by thousands of samples in order to get the proper probability distribution. Moreover, the distribution inferred by the BI approach is usually heavily affected by the initial assumptions, e.g., by the probability distribution assignment to all the parameters; therefore this approach could not guarantee that the obtained value is the absolute optimum one, but only the optimum of a solution subspace defined by the first guess choice, and this can dramatically affect not only the efficiency but even the robustness of all the procedure.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modified Bayesian Optimization Algorithm for Sparse Linear Antenna Design

Ha¹,

Pirinoli²,

Zich³

et al. 2013

PIER B

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Abstract-In this paper, a modified Bayesian Optimization Algorithm (BOA), named M-BOA, is proposed to introduce a suitable mutation scheme for the traditional procedure in order to speed up the convergence of the algorithm and to avoid it to be trapped in local minima or to stagnate in suboptimal solutions. The proposed algorithm has been applied both to a specific mathematical test function and to sparse linear antenna arrays design, showing outperforming capabilities not only with respect to the standard BOA, but also with respect to other assessed global optimization methods.

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Section: M-boa Based Linear Array Synthesismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modified Bayesian Optimization Algorithm for Sparse Linear Antenna Design

Ha¹,

Pirinoli²,

Zich³

et al. 2013

PIER B

View full text Add to dashboard Cite

show abstract

“…This work focuses on the synthesis of reconfigurable multiple patterns with fewer elements by selecting the common ones with optimized excitation amplitudes and phases. Some synthesis methods by using nonuniform element positions [16][17][18][19][20][21][22], or by applying thinning techniques [23][24][25], have been presented to effectively reduce the number of elements. However, these reviewed techniques are proposed for the synthesis of single-pattern arrays.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Sparse or Thinned Linear and Planar Arrays Generating Reconfigurable Multiple Real Patterns by Iterative Linear Programming

You¹,

Zhu

Tan³

et al. 2016

PIER

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Abstract-It is shown in this paper that the problem of reducing the number of elements for multiplepattern arrays can be solved by a sequence of reweighted 1 optimizations under multiple linear constraints. To do so, conjugate symmetric excitations are assumed so that the upper and lower bounds for each pattern can be formulated as linear inequality constraints. In addition, we introduce an auxiliary variable for each element to define the common upper bound of both the real and imaginary parts of multiple excitations for different patterns, so that only linear inequality constraints are required. The objective function minimizes the reweighted 1 -norm of these auxiliary variables for all elements. Thus, the proposed method can be efficiently implemented by the iterative linear programming. For multiple desired patterns, the proposed method can select the common elements with multiple set of optimized amplitudes and phases, consequently reducing the number of elements. The radiation characteristics for each pattern, such as the mainlobe shape, response ripple, sidelobe level and nulling region, can be accurately controlled. Several synthesis examples for linear array, rectangular/triangular-grid and randomly spaced planar arrays are presented to validate the effectiveness of the proposed method in the reduction of the number of elements.

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“…More recently, new techniques such as matrix pencil methods [12]- [14], constrained optimization methods [15]- [17], compressive sensing methods [18]- [21], and analytical methods [22]- [23] have been successfully applied in designing arrays with a reduced number of elements. The matrix pencil methods yield arrays that have elements in arbitrary positions and are useful when the elements are not constrained to predefined positions.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Linear and Planar Arrays With Minimum Element Selection

Nongpiur

Shpak

2014

IEEE Trans. Signal Process.

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Abstract-A new method for the synthesis of linear and planar arrays having prescribed beamwidth and sidelobe levels and a minimum number of elements is proposed. In the method, the number of elements in an array is minimized while constraining the amplitude-response error in the mainlobe region, the attenuation in the sidelobe region, and the array dimensions. An iterative constrained optimization method is used where the amplituderesponse error is linearly approximated at each iteration while concurrently minimizing a re-weighted L1 norm of the array coefficients. To ensure robustness of the array, we constrain a sensitivity parameter, namely, the white noise gain, to be above a prescribed level. Furthermore, the method also provides the additional flexibility of controlling the array dimensions, symmetry properties, and element positions of the array. Two variants have been developed: In the first variant, both the array coefficients and the positions of the elements are optimized; in the second variant only the array coefficients are optimized while the elements are fixed at predefined positions. Experimental comparisons with several state-of-the-art competing methods show that the proposed method provides greater flexibility of controlling the robustness, beampattern response error, array dimensions, and element positions while at the same time the number of elements is less than or equal to that of the competing methods.

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Reducing the Number of Elements in Linear and Planar Antenna Arrays With Sparseness Constrained Optimization

Cited by 94 publications

References 13 publications

Modified Bayesian Optimization Algorithm for Sparse Linear Antenna Design

Modified Bayesian Optimization Algorithm for Sparse Linear Antenna Design

Synthesis of Sparse or Thinned Linear and Planar Arrays Generating Reconfigurable Multiple Real Patterns by Iterative Linear Programming

Synthesis of Linear and Planar Arrays With Minimum Element Selection

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