2-D DOA Estimation Based on Sparse Linear Arrays Exploiting Arbitrary Linear Motion

In direction of arrival (DOA) estimation exploiting array motions, conventional designs consider sparse arrays moving along or perpendicular to the array direction, and only the second-order statistics are exploited. As the estimation performance is highly related to the synthetic virtual structure and source distribution, moving direction will definitely influence the performance. In this paper, we propose a generalized array synthesis method with a sparse linear array moving towards an arbitrary direction based on high-order difference co-arrays, where the physical array and moving sampling motions are specifically designed with improved degrees of freedom (DOFs). Unambiguity property of this arbitrary moving model is analyzed, followed by an affine transformation based unitary ESPRIT method for 2-D DOA estimation. The Cramér-Rao bound is derived and an iterative moving direction optimization method is then proposed for further performance improvement. Simulation results are provided to verify the superior performance of the synthetic model, showing the impact of moving direction on the performance, and a better performance is achieved by optimizing the moving direction iteratively.