In this study, we propose a method for direction of arrival (DOA) estimation of mixed circular and noncircular coherently distributed (CD) sources under impulsive noise. The existing methods estimate the angular parameters of circular and noncircular CD sources simultaneously under Gaussian noise, but they encounter issues such as low resolution at small angular separation and performance degradation under impulsive noise. To overcome these challenges, we first construct an augmented norm covariance to mitigate the effects of impulsive noise, and then we present a high-resolution DOA estimation approach that handles circular, general noncircular, and rectilinear CD sources separately by analyzing the constraints satisfied by the generalized steering vector. We evaluate the superior performance of our proposed method through numerical simulations and compare it with the derived Cramér-Rao bound for the considered scenarios.

In this work, a computationally efficient evolutionary algorithm is proposed for estimating the direction of arrival (DOA) of coherently distributed sources corrupted by additive impulsive noise. The typical method, such as distributed signal parameter estimation method, requires a two-dimensional spectral peak search, and cannot allow for coherent signals. The proposed method in this paper employs an infinite norm normalization combined with the maximum likelihood criteria. The resulting cost function is solved using the introduced multimodal quantum-inspired benchmarking algorithm, which obviously reduces the computational complexity without grid errors. The Cramér-Rao bound for the considered DOA estimation problem is also derived. Finally, numerical simulations are conducted to show that our algorithm can perform superior robustness and allow for coherent signals without any additional process compared to alternative approaches for the considered scenarios. In addition, the proposed scheme can be popularized to address other DOA estimation problems and promote the development of DOA estimation.

In this letter, we propose a robust direction of arrival (DOA) estimation method for mixed circular and noncircular incoherently distributed (ID) sources in impulsive noise. This approach incorporates an outlier-robust extended bounded nonlinear covariance to alleviate the impact of impulsive noise, and we demonstrate its applicability for DOA estimation of ID sources. Subsequently, we determine the DOAs and angular spreads of mixed circular and noncircular ID sources in a closed form via array shift invariance. Numerical simulations illustrate that our proposed method outperforms alternatives in terms of both effectiveness and robustness.

In this work, a low-complexity direction of arrival (DOA) estimation method is proposed for coherently distributed (CD) sources considering gain-phase errors in a massive multiple-input multiple-output (MIMO) system. To alleviate the computational burden of calculating the covariance matrix and its eigen decomposition in massive MIMO systems, a Nyström-based signal subspace estimation technique is proposed, which only requires the computation of two submatrices of the covariance matrix. Furthermore, the DOAs, angular spreads and gain-phase coefficients are obtained in closed form via array shift invariance, provided that the partly antennas are well-calibrated. In addition, we derive the Cramér-Rao bound for the discussed problem. Finally, the proposed method performs attractive effectiveness and robustness compared to other methods through numerical simulations.

To address the challenges posed by the high cost and power consumption of massive multi-input multi-output (MIMO) receive array, as well as the limitations of point-source-based direction-of-arrival (DOA) estimation methods when applied to incoherently distributed (ID) sources, a one-bit DOA estimation approach tailored for ID sources is proposed in this work. First, we formulate the covariance fitting equation using the correlation between one-bit covariance matrix and its normalized unquantized counterpart. Due to its nonconvexity, we discretize the spatial domain and transform it into a convex second-order cone programming problem. Then, we derive a cyclic minimization algorithm that iteratively minimizes the resulting cost function in closed forms. Using a first-order Taylor expansion, we reconstruct the noiseless signal covariance matrix, thereby accurately estimating the angular spread. Furthermore, we derive the approximate Cramér-Rao bound. The superiority in terms of accuracy and efficiency is demonstrated through simulation results.

Coherently distributed (CD) sources are typically characterized by their direction of arrival (DOA) and angular spread. Traditional distributed signal parameter estimation (DSPE) methods for CD sources rely on a computationally intensive two-dimensional spectral search, which limits their suitability for real-time applications. In this letter, we propose a simple yet effective method to decouple the angular parameters of CD sources. By projecting the sample covariance matrix onto Toeplitz space, DOA information is approximately extracted. The DOAs are then estimated by a polynomial root-finding approach, followed by a one-dimensional DSPE spectral search to determine the angular spread. Simulation results demonstrate that the proposed method achieves performance comparable to existing approaches while significantly enhancing computational efficiency.