Adaptive Gridless Covariance-Matching DOA Estimation for Coprime Array Under Impulsive Noise

In symmetric α-stable (SαS)-distributed impulsive noise, classical direction of arrival (DOA) estimators degrade markedly due to heavy-tailed statistics. To address this, we propose an adaptive, gridless DOA method for coprime arrays. First, we design a condition-selection phased fractional lower-order moment scheme that determines the fractional order by minimizing the condition number, thereby accommodating varying noise intensities without prior knowledge of the characteristic index α. Then, leveraging the virtual uniform linear array formed by the coprime difference coarray, we establish a covariance-matching gridless sparse reconstruction framework to estimate DOAs, eliminating angular grid mismatch and internalizing regularization selection within a constrained optimization, thus avoiding manual tuning. Numerical simulations indicate that, across a broad range of generalized signal-to-noise ratios, snapshot counts, and noise intensities, the proposed method delivers overall gains in estimation accuracy and resolution over existing approaches. Real-world validation using a millimeter-wave radar sensor further verifies its detection performance and practical utility.