Application of Sparse Representation to Bartlett Spectra for Improved Direction of Arrival Estimation

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.

Automated summary

This study introduces a new technique for high-resolution direction of arrival estimation, showing through Monte Carlo simulations that it achieves accurate estimations and outperforms existing algorithms in certain scenarios.