ELEDIA@UniTN

ELEDIA Turns 20!
ELEDIA Turns 20! It was back in October-November 2000 that the ELEDIA brand began to circulate in an official form…
ELEDIA Artificial Intelligence Research Blog is online!
The ELEDIA Artificial Intelligence Research Blog (E-AIR Blog) has been launched! Check out our latest advancements on Artificial Intelligence as applied…
Dr. R. J. MAILLOUX awarded "Bruno Kessler" Honorary Professor
The ELEDIA Research Center is pleased to announce that Dr. Robert J. MAILLOUX will receive the title of Honorary Professor…

Sparse arrays synthesis by means of Bayesian Compressive Sensing (BCS)

 
 
Many applications including radio base stations, television broadcasting and satellite communications require the design of antennas exhibiting radiating performances that cannot be obtained by a single antenna. In order to satisfy these design constraints, it is thus necessary to employ antenna arrays (i.e. antennas composed by a large number of radiating elements). However, since severe limitations are usually enfoced on the cost, the weight and the hardware and software complexity of the radiating system, the designed arrays must often comprise a minimum number of elements. This objective can be accomplished by using, instead of a regular arrangement, a properly designed non-uniform antenna layout.
   
Satellite systems for wireless communications, astronomic observations, navigation, and earth and weather observation, antennas for base stations and television broadcasting.

 
Satellite Regional Coverage

 
Weather Observation
 

The activities of the members of the ELEDIA Research Center are focused on the development of innovative strategies for the design of sparse antenna arrays. In order to achieve this objective, a method based on the exploitation of Bayesian Compressive Sensing (BCS) has been developed and applied to:

  1. The synthesis of arrays characterized by different layouts (e.g linear and planar arrays).
  2. The design of antenna arrays with pencil beam and shaped patterns.
  3. The synthesis of arrays imposing geometrical constraints on the elements displacement (e.g. "holes" in the array layout).

The developed methodology allows the numerical-efficient synthesis of large antenna arrays with a high saving in terms of array elements compared with analogous state-the-art techniques for the synthesis of uniformly-spaced antenna arrays.

 
Synthesized Beam Pattern

 
Sparse Array Geometry

Keywords: Array Synthesis, Sparse arrays, Bayesian Compressive Sampling


See Also
  • G. Oliveri and A. Massa, "Bayesian compressive sampling for pattern synthesis with maximally sparse non-uniform linear arrays," IEEE Trans. Antennas Propag., vol. 59, no. 2, pp. 467-481, Feb. 2011. doi:10.1109/TAP.2010.2096400
  • G. Oliveri, M. Carlin, and A. Massa, "Complex-Weight Sparse Linear Array Synthesis by Bayesian Compressive Sampling," IEEE Trans. Antennas Propag., vol. 60, no. 5, pp. 2309-2326, May 2012.
  • G. Oliveri, E. T. Bekele, F. Robol, and A. Massa, "Sparsening conformal arrays through a versatile BCS-based method," IEEE Trans. Antennas Propag., vol. 62, no. 4, pp. 1681-1689, Apr. 2014.
    doi:10.1109/TAP.2013.2287894
  • F. Viani, G. Oliveri, and A. Massa, "Compressive sensing pattern matching techniques for synthesizing planar sparse arrays," IEEE Trans. Antennas Propag., vol. 61, no. 9, pp. 4577-4587, Sept. 2013.
    doi:10.1109/TAP.2013.2267195.