Exploration on 2D DOA Estimation of Linear Array Motion: Uniform and Sparse Circular Motion

It is a challenging and attractive topic to extend the aperture dimension of linear arrays with convenient configuration for two-dimensional (2D) direction of arrival (DOA) estimation. In this paper, exploration on the circular motion of linear arrays is carried out to achieve joint superiorities of the simple configuration in the linear array and the powerful capability of estimation in the circular array. Two signal models with uniform and sparse sampling are synthesized on the basis of synthetic processing techniques, respectively, for extending the aperture dimension. The virtual uniform concentric circular array (VUCCA) is achieved by the uniform sampling, and an efficient algorithm named polynomial expansion-based decoupled reduced-complexity multiple signal classification (PE-DRC MUSIC) is proposed correspondingly to realize high-accuracy and low-complexity 2D DOA estimation by making full use of the properties of polynomial expansion. On the other hand, the virtual sparse concentric circular array (VSCCA) is realized by innovatively exploiting the sparsity of the concentric circular array and sparse sampling. The difference coarray in VSCCA is investigated for a higher degree of freedom (DOF), and a 2-exponent linear array with an efficient sampling method is proposed pertinently. Furthermore, the properties of estimation ambiguity and mutual coupling (MC) are comprehensively analyzed in both the VUCCA and VSCCA. Simulations show that the underdetermined 2D DOA estimation can be achieved just by a single linear array. Moreover, our proposed VUCCA with PE-DRC MUSIC realizes high-accuracy but low-complexity estimation and is more robust while encountering the effect of MC. Besides, the VSCCA has a higher achievable DOF and better estimation performance than the rotating nested sparse circular array.