Traditional direction of arrival (DOA) estimation algorithms fail to account for the impact of nonuniform noise in practical applications, leading to model mismatch and inaccurate DOA estimation. To overcome this issue, we propose a robust gridless sparse iterative estimation-based DOA algorithm using coprime arrays. First, the received signal model is constructed, and adaptive reconstruction of the covariance matrix is achieved by setting the diagonal elements to their minimum values based on the characteristics of nonuniform noise. Subsequently, leveraging the sparsity of DOAs in the spatial domain, interpolation and overcomplete representation of the second-order received signals in the virtual domain are performed. A covariance fitting optimization problem is then formulated using a gridless sparse iterative estimation method, achieving high-precision DOA estimation and enhanced degrees of freedom. Furthermore, we employ an alternating projection strategy in the optimization process, effectively reducing the computational cost associated with complex iterative procedures. Numerical simulations validate the effectiveness and robustness of the proposed algorithm, and its performance is further demonstrated using real-world measurements from millimeter-wave radar sensors.

In the field of array signal processing, accurate direction of arrival (DOA) estimation is crucial for handling both coherent and incoherent mixed signal sources. This paper proposes a deep learning-enhanced atomic norm minimization (DLEANM) algorithm, designed to improve DOA estimation for coprime arrays. The algorithm first models the mixed signal scenarios realistically and employs interpolation to transform coprime arrays into virtual uniform linear arrays, which facilitates the formulation of the atomic norm minimization problem. Solving this problem reconstructs an augmented Hermitian Toeplitz covariance matrix. To further enhance the accuracy of DOA estimation, we developed a multi-scale convolutional neural network with an attention mechanism, which uses the covariance matrix as input to better capture signal variations across different frequencies and spatial distributions. By processing multi-scale inputs in parallel, the model improves adaptability and robustness in estimating mixed signal sources. Simulation results show that, under equivalent SNR and snapshot conditions, the DLEANM algorithm achieves lower estimation errors, more accurate multi-target DOA estimation, and relatively faster computation speed compared to conventional methods. Field experiments further confirm the effectiveness of the proposed algorithm. Therefore, after training, the algorithm is able to accurately identify the directions of unknown signals received by sensor arrays.

This paper proposes a fast direction of arrival (DOA) estimation method based on positive incremental modified Cholesky decomposition atomic norm minimization (PI-CANM) for augmented coprime array sensors. The approach incorporates coprime sampling on the augmented array to generate a non-uniform, discontinuous virtual array. It then utilizes interpolation to convert this into a uniform, continuous virtual array. Based on this, the problem of DOA estimation is equivalently formulated as a gridless optimization problem, which is solved via atomic norm minimization to reconstruct a Hermitian Toeplitz covariance matrix. Furthermore, by positive incremental modified Cholesky decomposition, the covariance matrix is transformed from positive semi-definite to positive definite, which simplifies the constraint of optimization problem and reduces the complexity of the solution. Finally, the Multiple Signal Classification method is utilized to carry out statistical signal processing on the reconstructed covariance matrix, yielding initial DOA angle estimates. Experimental outcomes highlight that the PI-CANM algorithm surpasses other algorithms in estimation accuracy, demonstrating stability in difficult circumstances such as low signal-to-noise ratios and limited snapshots. Additionally, it boasts an impressive computational speed. This method enhances both the accuracy and computational efficiency of DOA estimation, showing potential for broad applicability.

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.