부산대학교 도서관

Although Direction-of-Arrival (DoA)-estimation has been well-researched in the recent decades, some specific scenarios with certain requirements are not yet delved in depth. In this letter, we introduce an innovative application for gridless DoA-estimation, called Orthonormal Atomic Norm Minimization (OANM). By applying OANM in case of 1-dimensional DoA-estimation with prior information aided, atomic separation is shown to enjoy a higher degree of freedom by outcoming with a lower DoA-estimation error compared to other methods that are also compressive sensing-based. OANM shows its power by utilizing a single snapshot of the received signal only and estimates all the DoAs of each mobile user in a noisy environment with relatively low error but also low computational complexity when atoms are close in separation.