DOA Estimation Using Compressed Sparse Array

Sparse arrays, such as nested arrays and coprime arrays, can achieve a high number of degrees of freedom (DOFs) for direction of arrival (DOA) estimation with a reduced number of antennas. On the other hand, the compressive measurement method provides an effective way to reduce the number of frontend circuit chains. In this paper, we generalized current works on the two categories of methods to a compressed sparse array (CSA) scheme, which combines the compressive measurement method, and the sparse array together to significantly reduce the system complexity. After introducing the proposed scheme, the Cramér-Rao bound (CRB) of the proposed CSA scheme is derived. We then determine the corresponding existing conditions of the CRB, based on which the number of DOFs is derived and examined for the first time. It is proved that, for a CSA which compressed the output of an L-element sparse array to M <; L chains, a higher number of DOFs is obtained as compared to that of the M-element array with the same sparse structure. Furthermore, the DOA estimation accuracy using the M-chain CSA is higher than that using the M-element sparse array due to the extended array aperture. Numerical simulations verify the superiority of the proposed CSA scheme.