Elbir, Ahmet Musab2020-04-302020-04-3020190923-60821573-0824https://doi.org/10.1007/s11045-018-0623-zhttps://hdl.handle.net/20.500.12684/3531Elbir, Ahmet M./0000-0003-4060-3781WOS: 000485972700002In sparsity-based optimization problems, one of the major issue is computational complexity, especially when the unknown signal is represented in multi-dimensions such as in the problem of 2-D (azimuth and elevation) direction-of-arrival (DOA) estimation. In order to cope with this issue, this paper introduces a new sparsity structure that can be used to model the optimization problem in case of multiple data snapshots and multiple separable observations where the dictionary can be decomposed into two parts: azimuth and elevation dictionaries. The proposed sparsity structure is called joint-block-sparsity which enforces the sparsity in multiple dimensions, namely azimuth, elevation and data snapshots. In order to model the joint-block-sparsity in the optimization problem, triple mixed norms are used. In the simulations, the proposed method is compared with both sparsity-based techniques and subspace-based methods as well as the Cramer-Rao lower bound. It is shown that the proposed method effectively solves the 2-D DOA estimation problem with significantly low complexity and sufficient accuracy.en10.1007/s11045-018-0623-zinfo:eu-repo/semantics/closedAccessDirection of arrival estimationJoint-block-sparsitySeparable observationsTriple mixed normsArticle30416591669WOS:000485972700002Q2Q2