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Combining Matrix Design for 2D DoA Estimation with Compressive Antenna Arrays using Stochastic Gradient Descent

: Pawar, Sankalp-Prakash; Semper, Sebastian; Römer, Florian


Sanei, Saeid (General Chair) ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
ICASSP 2019, IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings : May 12-17, 2019, Brighton
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-4799-8131-1
ISBN: 978-1-4799-8132-8
International Conference on Acoustics, Speech, and Signal Processing (ICASSP) <44, 2019, Brighton>
Conference Paper
Fraunhofer IZFP ()
doa estimation; Compressed Sensing (CS); spatial correlation function (SCF)

Recently, compressive antenna arrays have been considered for direction of arrival (DoA) estimation with reduced hardware complexity. By utilizing compressive sensing, such arrays employ a linear combining network to combine signals from a larger set of antenna elements in the analog RF domain. In this paper, we develop a design approach based on the minimization of error between spatial correlation function (SCF) of the compressive and the uncompressed array resulting in the estimation performance of the two arrays to be as close as possible. The proposed design is based on grid-free stochastic gradient descent (SGD) optimization. In addition to a low computational cost for the proposed method, we show numerically that the resulting combining matrices perform better than the ones generated by a previous approach and combining matrices generated from a Gaussian ensemble.