Maximally Sparse Arrays Via Sequential Convex Optimizations

Prisco, G.; D’Urso, M.

doi:10.1109/lawp.2012.2186626

Cited by 158 publications

(146 citation statements)

References 16 publications

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“…Recently, the iterative reweighted 1 -norm optimization was presented in [30] to approach as closely as possible to 0 -norm for enhanced sparsity. This method has been successfully applied to the sensors selection for single-pattern arrays in [31][32][33]. Now, we extend the idea to enhance the sparsity of the multiple-pattern array synthesis.…”

Section: Element Selection Using Iterative Reweighted 1 Optimizationmentioning

confidence: 98%

“…The iterative reweighted 1 optimization was presented in [30], and recently this idea was used to reduce the number of elements for a single focused beam or shaped pattern, by developing the iterative second-order cone programming (SOCP) in [31][32][33] or sequential compressive sensing (CS) approach in [34]. We now apply this idea to reduce the number of elements for multiple-pattern arrays by selecting the best common elements, each with multiple optimized excitations, under multiple power pattern requirements that are all given by upper and lower bounds.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Element Selection Using Iterative Reweighted 1 Optimizationmentioning

confidence: 98%

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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“…As for other applications to array signal processing, compressed sensing has been applied to the problem of synthesizing a desired far-field beam-pattern by controlling sensors' positions and array weights with the minimum number of sensors, which is called the maximally sparse array [185]- [188]. Also, it has been applied to the diagnosis of antenna arrays [189] and some radar applications [190]- [192], assuming sparsities of failure antenna elements and reflectivity functions of targets, respectively.…”

Section: Some Other Topicsmentioning

confidence: 99%

A User's Guide to Compressed Sensing for Communications Systems

Hayashi

Nagahara

Tanaka

2013

IEICE Trans. Commun.

203

110

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SUMMARYThis survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sensing as an underdetermined linear system with a prior information that the true solution is sparse, and explain the sparse signal recovery based on 1 optimization, which plays the central role in compressed sensing, with some intuitive explanations on the optimization problem. Moreover, we introduce some important properties of the sensing matrix in order to establish the guarantee of the exact recovery of sparse signals from the underdetermined system. After summarizing several major algorithms to obtain a sparse solution focusing on the 1 optimization and the greedy approaches, we introduce applications of compressed sensing to communications systems, such as wireless channel estimation, wireless sensor network, network tomography, cognitive radio, array signal processing, multiple access scheme, and networked control.

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“…Generally, there are five sparse array antenna design methods that have been published; they can be classified as deterministic and generic algorithm methods [3][4][5][6][7][8][9], stochastic and probabilistic methods [10][11][12], general polynomial factorizations [13][14][15], combinatorial methods [16][17][18] and mutual coupling effects [19][20][21]. The combinatorial method, using cyclic difference sets (CDS), is a suitable design for a sparse array antenna with high efficiency, a simple process and minimal computation time compared with other sparse array methods.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Technique Using Combinatorial Cyclic Difference Sets and Binomial Amplitude Tapering for Linear Sparse Array Antenna Design

Sandi

Zulkifli²,

Rahardjo³

2016

AEM

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Reducing system complexity and cost in synthesizing a sparse array antenna design is a challenging task for practical communication systems, such as radar systems and space communication. In this paper, a hybrid technique to synthesize a linear sparse array antenna design is described. This technique is developed using two methods. The first method is a combinatorial approach that applies cyclic difference sets (CDS) integers to significantly reduce the number of antenna elements. The approach and procedure used to apply the new CDS method to configure a linear sparse array, with significant reduction of the spatial antenna dimension, is described. The second method, applied to the array result of the first method, is amplitude tapering using a binomial array approach to reduce the sidelobes level (SLL). The simulation and measurement results of the sample sparse array design showed that the SLL was reduced in comparison to the sparse array design using only the combinatorial CDS method.

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Maximally Sparse Arrays Via Sequential Convex Optimizations

Cited by 158 publications

References 16 publications

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

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

A User's Guide to Compressed Sensing for Communications Systems

A Hybrid Technique Using Combinatorial Cyclic Difference Sets and Binomial Amplitude Tapering for Linear Sparse Array Antenna Design

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