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Keywords
(11)
bayesian method
Compressive Sampling
Computational Efficiency
Indexing Terms
Linear Arrangement
Pattern Matching
Phased Array
Relevance Vector Machine
Sensitivity Analysis
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Uniform Linear Array
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Bayesian Compressive Sampling for Pattern Synthesis With Maximally Sparse NonUniform Linear Arrays
Bayesian Compressive Sampling for Pattern Synthesis With Maximally Sparse NonUniform Linear Arrays,10.1109/TAP.2010.2096400,IEEE Transactions on Ante
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Bayesian Compressive Sampling for Pattern Synthesis With Maximally Sparse NonUniform Linear Arrays
(
Citations: 6
)
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Giacomo Oliveri
,
Andrea Massa
A numericallyefficient technique based on the Bayesian
compressive sampling
for the design of maxi mallysparse linear arrays is introduced. The method is based on a probabilistic formulation of the array synthesis and it exploits a fast
relevance vector machine
for the problem solution. The proposed approach allows the design of linear arrangements fitting desired power patterns with a reduced number of nonuni formly spaced active elements. The numerical validation assesses the effectiveness and
computational efficiency
of the proposed approach as a suitable complement to existing stateoftheart techniques for the design of sparse arrays. Index Terms—Array synthesis, Bayesian
compressive sampling
(BCS), linear arrays, relevance vector machine, sparse arrays.
Journal:
IEEE Transactions on Antennas and Propagation  IEEE TRANS ANTENNAS PROPAGAT
, vol. 59, no. 2, pp. 467481, 2011
DOI:
10.1109/TAP.2010.2096400
Cumulative
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Citation Context
(5)
...While the first problem has been widely studied [2][3][7][14], only few techniques have been introduced for the solution of the latter [
16
]...
...In this framework, numerically inexpensive approaches, such as the steepest descent method, the iterative leastsquare technique, the simplex search, and the linear programming, were among the first methodologies applied to sparse array design [15][
16
]...
...However, these techniques exhibit some drawbacks in terms of flexibility, required apriori information, and final obtained performances [
16
]...
...An innovative approach for the synthesis of sparse arrays with prescribed pattern features has been recently proposed [
16
]...
...This methodology is based on the formulation of the sparse array synthesis problem as a “Compressive Sensing (CS) retrieval” one, in which the sparseness constraints are imposed on the final array layout [
16
]...
...Thanks to this approach, BCS sparse array synthesis has proved to be effective in dealing with standard and reference sparse array synthesis problems [
16
]...
...The problem of finding the sparsest (real and symmetric [
16
]) linear array with desired radiating properties can be cast in terms of a pattern matching one as follows [16]:...
...The problem of finding the sparsest (real and symmetric [16]) linear array with desired radiating properties can be cast in terms of a pattern matching one as follows [
16
]:...
...is the vector of the samples of the sparse array radiation pattern, λ is the wavelength, uk (k=1,..,K) are the matching angles, dn (n=0,…,N) are the allowed positions for the sparse array element, and χn is the Neumann’s number [3][
16
]...
...By modeling the radiation pattern as a Gaussian random variable [
16
], the above synthesis problem can be recasted in the framework of BCS to obtain the following equivalent one [16]: [ ]...
...By modeling the radiation pattern as a Gaussian random variable [16], the above synthesis problem can be recasted in the framework of BCS to obtain the following equivalent one [
16
]: [ ]...
...[18] and the estimated fidelity variance [
16
]...
...Following the RVM approach [17][18], this BCS problem is then solved by the following procedure [
16
]: 1. Input phase: define the reference pattern samples EREF, the set of admissible element locations dn (n=0,…,N), and the initial estimate of the fidelity variance; 2. Matrix Definition: calculate the problem matrix Φ, with Φ(k,n)=χn cos(2πdnuk/λ); 3. Hyperparameter Posterior Modes Estimation: find a and σ 2 according to the RVM procedure ...
... the RVM approach [17][18], this BCS problem is then solved by the following procedure [16]: 1. Input phase: define the reference pattern samples EREF, the set of admissible element locations dn (n=0,…,N), and the initial estimate of the fidelity variance; 2. Matrix Definition: calculate the problem matrix Φ, with Φ(k,n)=χn cos(2πdnuk/λ); 3. Hyperparameter Posterior Modes Estimation: find a and σ 2 according to the RVM procedure [
16
];...
Giacomo Oliveri
,
et al.
Synthesis of large sparse linear arrays by Bayesian Compressive Sensin...
...<{[SECTION]}>(a BCS ,σ 2� BCS ) has been determined [45], [
52
]...
Giacomo Oliveri
,
et al.
A BayesianCompressiveSamplingBased Inversion for Imaging Sparse Sca...
...In this work, two innovative design techniques are proposed, namely the CPM [4] and the BCS [
5
], aimed at synthesizing simple array architectures with high BCEs and low PSLs...
...Towards this end, a matching either on the weights [4] or on the pattern [
5
] of known DPSS sequences [3] is performed...
...The synthesis of sparse linear arrays characterized by the minimum number of radiating elements with real and symmetric excitations in order to obtain a pattern with the desired properties has been presented in [
5
]...
...found according to the procedure described in [
5
]; (iv) Array weights estimation  The optimal sparse weights are computed as...
Paolo Rocca
,
et al.
Innovative array designs for wireless power transmission
...Such computational solutions, together with the design nonregular [
16
] as well as multibeam [17] WPT arrays are currently under investigation...
Giacomo Oliveri
,
et al.
Array antenna architectures for solar power satellites and wireless po...
...Example are sparse array synthesis [
10
], [12], and wireless sensor networks [6]...
Marco Donald Migliore
,
et al.
Compressed sensing in electromagnetics: Theory, applications and persp...
References
(27)
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(
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C. A. Balanis
Published in 1982.
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(
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N. Balakrishnan
,
P. K. MURTHY
,
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Journal:
IEEE Transactions on Antennas and Propagation  IEEE TRANS ANTENNAS PROPAGAT
, vol. 27, no. 5, pp. 690696, 1979
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(
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Yanhui Liu
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Journal:
IEEE Transactions on Antennas and Propagation  IEEE TRANS ANTENNAS PROPAGAT
, vol. 56, no. 9, pp. 29552962, 2008
Linear arrays with variable interelement spacings
(
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M. Andreasen
Journal:
IEEE Transactions on Antennas and Propagation  IEEE TRANS ANTENNAS PROPAGAT
, vol. 10, no. 2, pp. 137143, 1962
Theory of unequallyspaced arrays
(
Citations: 61
)
A. Ishimaru
Journal:
IEEE Transactions on Antennas and Propagation  IEEE TRANS ANTENNAS PROPAGAT
, vol. 10, no. 6, pp. 691702, 1962
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Citations
(6)
Synthesis of large sparse linear arrays by Bayesian Compressive Sensing
(
Citations: 2
)
Giacomo Oliveri
,
Fabrizio Robol
,
Matteo Carlin
,
Andrea Massa
Published in 2011.
A BayesianCompressiveSamplingBased Inversion for Imaging Sparse Scatterers
(
Citations: 2
)
Giacomo Oliveri
,
Paolo Rocca
,
Andrea Massa
Journal:
IEEE Transactions on Geoscience and Remote Sensing  IEEE TRANS GEOSCI REMOT SEN
, vol. 49, no. 10, pp. 39934006, 2011
Imaging sparse scatterers through Bayesian Compressive Sensing methods
G. Oliveri
,
L. Poli
,
A. Massa
Conference:
International Conference on Electromagnetics in Advanced Applications  ICEAA
, 2011
Innovative array designs for wireless power transmission
Paolo Rocca
,
Giacomo Oliveri
,
Andrea Massa
Conference:
IEEE International Memory Workshop, IMW  IMW
, 2011
Array antenna architectures for solar power satellites and wireless power transmission
Giacomo Oliveri
,
Paolo Rocca
,
Andrea Massa
Conference:
General Assembly and Scientific Symposium  URSI
, 2011