DOA Estimation of Mixed Coherent and Uncorrelated Signals Under Unknown Number of Sources

The problem of estimating the direction of arrival (DOA) for a mixture of coherent and uncorrelated signals is addressed in this article. Most existing decoherence techniques reduce the array aperture, thereby limiting the number of resolvable sources. To solve this problem, this article proposes an improved least mean-squares (LMS) estimator by integrating the covariance matrix into its calculations. Moreover, we incorporate an $\ell _{{1}}$ -norm regularization term into the optimization framework to construct a novel objective function, thereby enhancing the accuracy of DOA estimation in multisource environments. A step further, in light of the substantial computational complexity associated with the interior point method, this article leverages the alternating direction method of multipliers (ADMM) to improve computational efficiency. Due to the elimination of the requirement for eigenvalue decomposition (EVD), this method does not require prior information on the number of sources. The simulation results indicate that the proposed method exhibits a lower root-mean-square error (RMSE) and a shorter running time.