eprintid: 705
rev_number: 9
eprint_status: archive
userid: 4
dir: disk0/00/00/07/05
datestamp: 2016-03-14 16:05:27
lastmod: 2016-03-14 16:05:27
status_changed: 2016-03-14 16:05:27
type: thesis
metadata_visibility: show
creators_name: Abdul, H.
title: A Multiresolution Approach for Direction of Arrival (DoA) Estimation in Linear Arrays based on Bayesian Compressive Sensing

ispublished: pub
subjects: MCS
subjects: MEA
full_text_status: public
keywords: Compressive Sensing, Direction-of-Arrival, Array Synthesis, Support Vector Machine.
abstract: The estimation of the direction of arrival of signals impinging on an antenna array is a problem of great interest in many applications, including mobile communications, target tracking and rescue systems. By sampling the angular region of interest with a large number of angular locations, the DoA estimation problem can be rewritten as a linear problem characterized by a sparse solution: hence, it is possible to exploit the Bayesian Compressive Sensing to reformulate the estimation problem in probabilistic terms, by looking for the most probable sparse signal fitting the data measured by the antenna array. 
One of the main disadvantages of this technique is that the estimated direction are confined to a grid, introducing a systematic estimation error due to the mismatch between the grid and the actual direction of arrival of the signals. The simplest solution is to define a very fine grid, but at the expense of a higher computational complexity. 
This project is aimed at overcoming this problem by defining an adaptive grid-refinement strategy that makes the grid fine only around the angular regions of interest. The objective is to increase the reliability of the estimations without affecting significantly the computational complexity of the overall system.

date: 2015-09-25
date_type: completed
institution: University of Trento
department: ELEDIA Research Center @ DISI
thesis_type: masters
referencetext: [1]	M. Carlin, P. Rocca, G. Oliveri, F. Viani, and A. Massa, "Directions-of-arrival estimation through Bayesian Compressive Sensing strategies," IEEE Trans. Antennas Propag., vol. 61, no. 7, pp. 3828-3838, Jul. 2013. 
[2]	M. Carlin, P. Rocca, G. Oliveri, and A. Massa, "Bayesian compressive sensing as applied to directions-of-arrival estimation in planar arrays," Journal of Electrical and Computer Engineering, Special Issue on "Advances in Radar Technologies", vol. 2013, pp. 1-12, 2013. 
[3]	M. Carlin, P. Rocca, "A Bayesian compressive sensing strategy for direction-of-arrival estimation," 6th European Conference on Antennas Propag. (EuCAP 2012), Prague, Czech Republic, pp. 1508-1509, 26-30 Mar. 2012. 
[4]	A. Massa, P. Rocca, and G. Oliveri, "Compressive sensing in electromagnetics - A review," IEEE Antennas and Propagation Magazine, pp. 224-238, vol. 57, no. 1, Feb. 2015. 
[5]	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. 
[6]	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. 
[7]	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. 
[8]	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. 
[9]	M. Carlin, G. Oliveri, and A. Massa, “Hybrid BCS-deterministic approach for sparse concentric ring isophoric arrays,” IEEE Trans. Antennas Propag., vol. 63, no. 1, pp. 378-383, Jan. 2015. 
[10]	L. Lizzi, F. Viani, M. Benedetti, P. Rocca, and A. Massa, "The M-DSO-ESPRIT method for maximum likelihood DoA estimation," Progress in Electromagnetic Research, vol. 80, pp. 477-497, 2008. 
[11]	M. Donelli, F. Viani, P. Rocca, and A. Massa, "An innovative multi-resolution approach for DoA estimation based on a support vector classification," IEEE Trans. Antennas Propag., vol. 57, no. 8, pp. 2279-2292, Aug. 2009. 
[12]	L. Lizzi, G. Oliveri, P. Rocca, and A. Massa, "Estimation of the direction-of-arrival of correlated signals by means of a SVM-based multi-resolution approach," IEEE Antennas Propag. Society International Symposium (APSURSI), Toronto, ON, Canada, pp. 1-4, 11-17 Jul. 2010. 

citation:   Abdul, H.  (2015) A Multiresolution Approach for Direction of Arrival (DoA) Estimation in Linear Arrays based on Bayesian Compressive Sensing.  Masters thesis, University of Trento.   
document_url: http://www.eledia.org/students-reports/705/1/Abstract.A517.pdf