An Effective Off-Grid DOA Estimation Algorithm Using Difference Coarrays with Limited Snapshots

A significant advantage of off-grid direction-of-arrival (DOA) estimation algorithms using difference coarrays is their ability to resolve more sources than the number of physical sensors. Current coarray-based off-grid DOA estimation algorithms experience a significant decline in estimation accuracy with limited snapshots. Moreover, most existing DOA estimation techniques exhibit a high computational complexity, limiting their practical implementation in real-time systems. To address these limitations, in this work, we propose a novel coarray-based off-grid DOA estimation algorithm that achieves a computationally efficient performance while maintaining a high estimation accuracy under snapshot-constrained conditions. The proposed algorithm first performs DOA estimation through coarray-augmented spatial smoothing multiple signal classification (SS-MUSIC), followed by noise suppression via multiplication with a constructed selection matrix. The off-grid angular deviations are sequentially refined based on the iterative correction mechanism. The disadvantage of a large number of snapshots requirement is overcome thanks to the combination of noise elimination and sequential angle refinement. Theoretical performance bounds are established through Cramér–Rao bound (CRB) analysis, while comprehensive simulations validate the estimation accuracy of the proposed algorithm and the robustness in off-grid scenarios.