Abstract
This paper proposes a novel sparse array design and an efficient algorithm for two-dimensional direction-of-arrival (2D-DOA) estimation. By analyzing the hole distribution in coprime arrays and introducing supplementary elements, we design a Complementary Coprime Planar Array (CCPA) that strategically fills key holes in the virtual array. This design enhances the array's continuous Degrees Of Freedom (DOFs) and virtual aperture, achieving improved performance in 2D-DOA estimation with fewer physical elements. The virtualization of the array further increases the available DOFs, while the hole-filling strategy ensures better spatial coverage and continuity. On the algorithmic side, we introduce a dimensionality-reduction root MUSIC algorithm tailored for uniform planar arrays after virtualization. By decomposing the two-dimensional spectral peak search into two one-dimensional polynomial root-finding problems, the proposed method significantly reduces computational complexity while maintaining high estimation accuracy. This approach effectively mitigates the challenges of 2D peak search, making it computationally efficient without sacrificing precision. Extensive simulations demonstrate the advantages of the proposed array and algorithm, including higher DOFs, reduced complexity, and superior estimation performance compared to existing methods. These results validate the effectiveness of the proposed framework in advancing sparse array design and signal processing for 2D-DOA estimation.