eprintid: 618
rev_number: 7
eprint_status: archive
userid: 4
dir: disk0/00/00/06/18
datestamp: 2014-05-07 08:35:23
lastmod: 2014-05-07 08:35:23
status_changed: 2014-05-07 08:35:23
type: thesis
metadata_visibility: show
creators_name: Eieta, H.
title: VERIFICA: IA for the evaluation of the robustness to amplitude/position errors for sparse arrays
ispublished: pub
subjects: TK
full_text_status: public
keywords: STDS
abstract: Interval Analysis (IA) consists of a set of rules and tools for the analysis and optimization of functions where the variables at hand are intervals of numbers and not single values as in classical arithmetical/optimization problems. For example, an interval of real values (a real interval) can be defined as a one-dimensional compact set (a segment) between two extreme points, namely the minimum and maximum interval values. 
Interval Analysis has several attractive features that can be exploited to perform a deep and accurate analysis in different situations dealing with uncertain, error and tolerances. More in detail: 
1. IA has an intrinsic capability to deal with uncertainties, always present when measurements are at hand.
2. Analytical equations and relationships can be easily reformulated and addressed by including intervals of numbers once the fundamentals of IA are known.
3. The bounds of a function when evaluated over an interval are determined in a straightforward manner without the need of evaluating the function on all (infinite) points of the interval.
In this project, the IA will be exploited to analyze the effect of tolerance on the amplitude and position errors considering various array configurations constituted by different number of elements, but aimed at generating the same beam pattern, in order to verify which configuration provides the highest robustness to errors. Such "sparse" configurations will be determined through a Bayesian Compressive Sampling-based synthesis technique.

date: 2014
date_type: published
institution: University of Trento
department: ELEDIA Research Center@DISI
thesis_type: masters
referencetext: [1]	N. Anselmi, L. Manica, P. Rocca, and A. Massa, "Tolerance analysis of antenna arrays through interval arithmetic," IEEE Transactions on Antennas and Propagation, vol. 61, no. 11, pp. 5496-5507, Nov. 2013
[2]	P. Rocca, L. Manica, N. Anselmi, and A. Massa, "Analysis of the pattern tolerances in linear arrays with arbitrary amplitude errors,"  IEEE Antennas Wireless Propag. Lett., vol. 12, pp. 639-642, 2013.
[3]	L. Manica, P. Rocca, N. Anselmi, and A. Massa, "On the synthesis of reliable linear arrays through interval arithmetic," IEEE International Symposium on Antennas Propag. (APS/URSI 2013), Orlando, Florida, USA, Jul. 7-12, 2013.
[4]	L. Manica, P. Rocca, G. Oliveri, and A. Massa, "Designing radiating systems through interval analysis tools," IEEE International Symposium on Antennas Propag. (APS/URSI 2013), Orlando, Florida, USA, Jul. 7-12, 2013.
[5]	M. Carlin, N. Anselmi, L. Manica, P. Rocca, and A. Massa, "Exploiting interval arithmetic for predicting real arrays performances - The linear case," IEEE International Symposium on Antennas Propag. (APS/URSI 2013), Orlando, Florida, USA, Jul. 7-12, 2013.
[6]	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.
[7]	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.
[8]	G. Oliveri, P. Rocca, and A. Massa, "Reliable Diagnosis of Large Linear Arrays - A Bayesian Compressive Sensing Approach," IEEE Trans. Antennas Propag., vol. 60, no. 10, pp. 4627-4636, Oct. 2012.
[9]	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.
[10]	G. Oliveri, E. T. Bekele, F. Robol, and A. Massa, "Sparsening conformal arrays through a versatile BCS-based method," IEEE Trans. Antennas Propag., in press, 2013. 

citation:   Eieta, H.  (2014) VERIFICA: IA for the evaluation of the robustness to amplitude/position errors for sparse arrays.  Masters thesis, University of Trento.   
document_url: http://www.eledia.org/students-reports/618/1/Abstract.A477.pdf