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 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.

In order to resolve direction finding problems in the impulse noise, a direction of arrival (DOA) estimation method is proposed. The proposed DOA estimation method can restrain the impulse noise by using infinite norm exponential kernel co-variance matrix and obtain excellent performance via the maximum-likelihood (ML) algorithm. In order to obtain the global optimal solutions of this method, a quantum electromagnetic field optimization (QEFO) algorithm is designed. In view of the QEFO algorithm, the proposed method can resolve the difficulties of DOA estimation in the impulse noise. Comparing with some traditional DOA estimation methods, the proposed DOA estimation method shows high superiority and robustness for determining the DOA of independent and coherent sources, which has been verified via the Monte-Carlo experiments of different schemes, especially in the case of snapshot deficiency, low generalized signal to noise ratio (GSNR) and strong impulse noise. Beyond that, the Cramér-Rao bound (CRB) of angle estimation in the impulse noise and the proof of the convergence of the QEFO algorithm are provided in this paper.

For bistatic multiple-input multiple-output (MIMO) radar, this paper presents a robust and direction finding method in strong impulse noise environment. By means of a new lower order covariance, the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood (ML) estimation method. A quantum equilibrium optimizer algorithm (QEOA) is devised to resolve the corresponding objective function for efficient and accurate direction finding. The results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations, e.g., locating coherent signal sources with very few snapshots in strong impulse noise. Other than that, the Cramér-Rao bound (CRB) under impulse noise environment has been drawn to test the capability of the presented method.

In order to solve the problem that the performance of traditional localization methods for mixed near-field sources (NFSs) and far-field sources (FFSs) degrades under impulsive noise, a robust and novel localization method is proposed. After eliminating the impacts of impulsive noise by the weighted outlier filter, the direction of arrivals (DOAs) of FFSs can be estimated by multiple signal classification (MUSIC) spectral peaks search. Based on the DOAs information of FFSs, the separation of mixed sources can be performed. Finally, the estimation of localizing parameters of NFSs can avoid two-dimension spectral peaks search by decomposing steering vectors. The Cramer-Rao bounds (CRB) for the unbiased estimations of DOA and range under impulsive noise have been drawn. Simulation experiments verify that the proposed method has advantages in probability of successful estimation (PSE) and root mean square error (RMSE) compared with existing localization methods. It can be concluded that the proposed method is effective and reliable in the environment with low generalized signal to noise ratio (GSNR), few snapshots, and strong impulse.