Efficient 2-D Direction of Arrival Estimation Using Adaptive Grid Integrated Dictionaries

This paper proposes a novel 2-D direction-of-arrival (DOA) estimation method based on sparse constraints and adaptive optimization, aiming to address issues such as grid mismatch and high computational complexity in traditional DOA estimation techniques. In contrast to using a traditional grid over distinct angles, combined angle information atoms are adopted to construct the dictionary, using a power-based covariance matrix fitting method to estimate the noise power and then to select the active regions. To compensate for off-grid bias effects, a 2-D dictionary learning procedure is proposed, which is able to yield accurate estimates even in single snapshot scenarios, without assuming prior information about the number of sources. Numerical simulations illustrate the preferable performance of the proposed method as compared to recent 2-D DOA estimation algorithms.