Abstract
A new technique for high-resolution direction of arrival estimation is presented. The method utilizes the traditional Bartlett spectra and sparse representation to locate emitters in single and multiple emitter scenarios. A method for selecting the sparse representation regularization parameter is also presented. Using Monte Carlo simulations, we show that the proposed approach achieves accurate direction of arrival (DOA) estimations that are unbiased and a variance that approaches the Cramer–Rao lower bound. We show that our method outperforms the popular MUSIC algorithm, and is slightly better than the sparse representation based L1-SVD algorithm when angular separation between emitters is small, signal SNR is low, and a small number of snapshots are used in DOA estimation.
Highlights
Multiple signal classification (MUSIC) algorithm, and is slightly better than the sparse representation based L1-singular value decomposition (SVD) algorithm when angular separation between emitters is small, signal SNR is low, and a small number of snapshots are used in direction of arrival (DOA) estimation
We have presented a new approach for the DOA estimation of multiple overlapping emitters incident on a sensor array
The approach is based on applying sparse representation to the Bartlett spectra obtained from the snapshots of the signals received by various array elements
Summary
Application of Sparse Representation to Bartlett Spectra for Improved. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Since World War II, obtaining accurate direction of arrival (DOA) estimation of the signals incident on an array of sensors has been an area of great interest. One of the earliest formulated DOA estimation methods that is still widely used today is Bartlett’s method [1]
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