Article

Regular sparse array direction of arrival estimation in one dimension

Citation

Knaepkens F, Cuyt A, Lee W & de Villiers DIL (2020) Regular sparse array direction of arrival estimation in one dimension. IEEE Transactions on Antennas and Propagation, 68 (5), pp. 3997-4006. https://doi.org/10.1109/TAP.2019.2963618

Abstract
Traditionally regularly spaced antenna arrays follow the spatial Nyquist criterion to guarantee an unambiguous analysis. We present a novel technique that makes use of two sparse non-Nyquist regularly spaced antenna arrays, where one of the arrays is just a shifted version of the other. The method offers several advantages over the use of traditional dense Nyquist spaced arrays, while maintaining a comparable algorithmic complexity for the analysis. Among the advantages we mention: an improved resolution for the same number of receivers and reduced mutual coupling effects between the receivers, both due to the increased separation between the antennas. Because of a shared structured linear system of equations between the two arrays, as a consequence of the shift between the two, the analysis of both is automatically paired, thereby avoiding a computationally expensive matching step as is required in the use of so-called co-prime arrays. In addition, an easy validation step allows to automatically detect the precise number of incoming signals, which is usually considered a difficult issue. At the same time, the validation step improves the accuracy of the retrieved results and eliminates unreliable results in the case of noisy data. The performance of the proposed method is illustrated with respect to the influence of noise as well to the effect of mutual coupling.

Keywords
Array antennas; direction of arrival estimation; sparse arrays

Journal
IEEE Transactions on Antennas and Propagation: Volume 68, Issue 5

StatusPublished
FundersUniversity of Antwerp
Publication date31/05/2020
Publication date online08/01/2020
Date accepted by journal25/11/2019
URLhttp://hdl.handle.net/1893/30584
ISSN0018-926X