In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA)
estimation that iteratively reduces the disturbance factors of the estimated data covariance …

For line spectrum estimation, we derive the maximum a posteriori probability estimator
where prior knowledge of frequencies is modeled probabilistically. Since the spectrum is …

In the area of array signal processing, direction of arrival (DoA) estimation is a widely
studied topic. In this estimation process the noise subspace of the received signal …

In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA)
estimation, referred to as multi-step knowledge-aided iterative estimation of signal …

In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA)
estimation, referred to as two-step knowledgeaided iterative estimation of signal parameters …

In this work, we propose a Krylov subspace-based algorithm for direction-of-arrival (DOA)
estimation, referred to as multi-step knowledge-aided iterative conjugate gradient (CG) …

In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA)
estimation using nested sensor arrays, referred to as multi-step knowledge-aided iterative …

For array processing, we consider the problem of estimating signals of interest, and their
directions of arrival (DOA), in unknown colored noise fields. We develop an estimator that …

In this work, we present direction-of-arrival (DoA) estimation algorithms based on the Krylov
subspace that effectively exploit prior knowledge of the signals that impinge on a sensor …

In this article, we investigate the performance of the recently proposed Direction-Of-Arrival
(DOA) estimator POWDER (Prior Orthogonally Weighted Direction EstimatoR). The method …