Super Fragmented Coprime Arrays for DOA Estimation
Abdul Hayee Shaikh, Xiaoguang Liu · IEEE Signal Processing Letters · 2025
The coprime arrays (CA) offer attractive merits in enhancing the degrees of freedom (DOF) and reducing the mutual coupling compared to the uniform linear arrays, which improves the direction of arrival (DOA) estimation performance. However, multiple holes in the difference co-array of the CA cause a loss in DOF and estimation accuracy. This letter presents a super fragmented coprime array (SFCA) configuration, which effectively reconfigures the sensor locations of the existing fragmented coprime design and further increases the inter-subarray spacings. This minimizes the occurrence of holes and enhances the DOF, with the capability of mitigating mutual coupling effects almost identical to the fragmented coprime structure. The SFCA enjoys closed-form expressions for precise sensor locations and calculating the DOF. Simulation results confirm the benefits of the proposed SFCA over other coprime designs.