Single Snapshot DOA Estimation Based on Compressive Sensing Using Co-Prime Sparse Array
Kankanala Srinivas, Puli Kishore Kumar, Saurav Ganguly · 2024
The concept of compressive sensing (CS) introduces a fresh paradigm applicable to fields where a solution to an underdetermined system of linear equations is required. Direction of arrival (DOA) estimation is one such application, where multiple sources are pinpointed using a sparsity constraint. Compared with uniform linear arrays (ULA), sparse arrays can create a larger aperture and offer a higher degree of freedom (DOF). A coprime array is one type of sparse array, composed of two linear arrays, and it offers numerous DOFs compared to conventional sparse arrays. In this paper, DOA estimation is enhanced by incorporating the coprime array concept with the ULA and integrating it into the CS framework. More specifically, using the coprime array concept, a low-dimensional kernel compresses the received signals from the ULA. Subsequently, these compressed signals are utilized for estimation of high-resolution DOA. Various simulation runs are performed to confirm the superiority of the suggested coprime sparse array, focusing on failure rate,, root mean square error (RMSE), and mean square error (MSE).