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A new method is developed for the Direction of Arrival (DOA) estimation of wideband sources by refining the initial estimates and posing the super‐resolution theory in a non‐convex optimization problem. A gradient‐projection method is conducted for the accurate estimation of DOAs. The proposed method can be performed by any non‐uniform linear array with no need for any focusing matrices. Moreover, a greedy search algorithm is employed to obtain initial estimates for super‐resolution implementation. Numerical simulations show the outstanding accuracy of the proposed method and more robustness to noise compared to some well‐known methods. It is also more effective for DOA estimation of adjacent sources. We develop a new method for the Direction of Arrival (DOA) estimation of wideband sources by refining the initial estimates and posing the super‐resolution theory in a non‐convex optimization problem.

This article proposes a deep neural network (DNN)‐based direct‐path relative transfer function (DP‐RTF) enhancement method for robust direction of arrival (DOA) estimation in noisy and reverberant environments. The DP‐RTF refers to the ratio between the direct‐path acoustic transfer functions of the two microphone channels. First, the complex‐value DP‐RTF is decomposed into the inter‐channel intensity difference, and sinusoidal functions of the inter‐channel phase difference in the time‐frequency domain. Then, the decomposed DP‐RTF features from a series of temporal context frames are utilized to train a DNN model, which maps the DP‐RTF features contaminated by noise and reverberation to the clean ones, and meanwhile provides a time‐frequency (TF) weight to indicate the reliability of the mapping. The DP‐RTF enhancement network can help to enhance the DP‐RTF against noise and reverberation. Finally, the DOA of a sound source can be estimated by integrating the weighted matching between the enhanced DP‐RTF features and the DP‐RTF templates. Experimental results on simulated data show the superiority of the proposed DP‐RTF enhancement network for estimating the DOA of the sound source in the environments with various levels of noise and reverberation.

Conventional direction-of-arrival (DOA) estimation methods are primarily used in point source scenarios and based on array signal processing. However, due to the local scattering caused by sea surface, signals observed from radar antenna cannot be regarded as a point source but rather as a spatially dispersed source. Besides, with the advantages of flexibility and comparably low cost, synthetic aperture radar (SAR) is the present and future trend of space-based systems. This paper proposes a novel DOA estimation approach for SAR systems using the simulated radar measurement of the sea surface at different operating frequencies and wind speeds. This article’s forward model is an advanced integral equation model (AIEM) to calculate the electromagnetic scattered from the sea surface. To solve the DOA estimation problem, we introduce a convolutional neural network (CNN) framework to estimate the transmitter’s incident angle and incident azimuth angle. Results demonstrate that the CNN can achieve a good performance in DOA estimation at a wide range of frequencies and sea wind speeds.

Compared with the traditional two‐step localization methods, the direct position determination (DPD) method is more robust at low signal‐to‐noise ratio. However, to guarantee the optimal location result, the computational complexity of DPD with grid search is too high, especially for moving targets. Therefore, a new DPD method is proposed for moving targets with low computational complexity. First, using a proposed cost function, the position information is obtained from the received array signals directly, which enhances the accuracy of localization. Second, utilizing the method of successional difference the velocity is extracted, which avoid the multi‐dimensional grid search and reduces the computational complexity. Simulation results demonstrate that the proposed method reduces the computational complexity greatly and outperforms the conventional methods. This letter proposes a new DPD method for moving target based on DOA. First, using a proposed cost function, the position information is obtained from the received array signals directly, which enhances the accuracy of localization. Second, utilizing the method of successional difference the velocity is extracted, which avoid the multi‐dimensional grid search and reduces the computational complexity. Simulation results demonstrate that the proposed method reduces the computational complexity greatly and outperforms the conventional methods.

This letter presents a method to optimize the estimation of direction of arrival (DOA) using the uniform linear array (ULA) with one‐bit signals in the presence of non‐uniform noise. With the Toeplitz properties and the rank characteristics of the signal subspace matrix formed by the ULA, the alternating minimization (AM) method is employed to optimize the estimation problem. To address this challenge further, we utilize the singular value thresholding (SVT) method and the approximate projection method. The angle deviations caused by non‐uniform noise can be effectively corrected and a significant improvement in the estimation performance can be achieved. The feasibility and effectiveness of the proposed method are demonstrated through simulation results. The alternating minimization method is employed to optimize the estimation of direction of arrival using the uniform linear array (ULA) with one‐bit signals in the presence of non‐uniform noise. The optimization problem is established using the Toeplitz properties and rank characteristics of the ULA's signal subspace matrix. The proposed method effectively correct the angle deviations caused by non‐uniform noise, resulting in a significant improvement in the estimation performance.

Coprime array can provide high degrees of freedom by using difference–sum co‐array under the same physical sensors, and can estimate the source under uncertain conditions. A special coprime array structure is proposed in this paper. Its difference–sum co‐array can improve the degrees of freedom. Finally, the proposed array structure estimation is used for direction‐of‐arrival. Simulation results and analysis show that the proposed array structure can effectively improve the accuracy of direction‐of‐arrival estimation. Under the same signal‐to‐noise ratio and snapshot number, it has lower root mean square error and higher estimation accuracy than other methods. A special coprime array structure is proposed in this paper. Its difference–sum co‐array can improve the degree of freedom. Simulation results and analysis show that the proposed array structure can effectively improve the accuracy of direction‐of‐arrival estimation.

ABSTRACT High‐accuracy direction of arrival (DOA) estimation remains challenging for unmanned aerial vehicle (UAV) array platforms due to stringent payload constraints. In this paper, we propose a motion‐based synthetic aperture strategy in which a linear array is controlled to move uniformly and sample multiple single snapshots within the temporal coherence period (TCP), thereby enabling two‐dimensional (2D) DOA estimation under such restrictive conditions. Furthermore, an Improved Grey Wolf Optimisation (IGWO)‐based calibration method is introduced to correct position errors induced by array motion, ensuring accurate angle estimation.

Two‐dimensional (2D) direction‐of‐arrival (DOA) estimation is crucial in array signal processing. Compressed sensing (CS) provides a superior alternative to spatial spectrum estimation algorithms by enabling 2D DOA estimation of correlated sources from single snapshot data. However, the grid mismatch effect inherent in grid‐based CS algorithms impacts estimation accuracy. Despite recent advancements, the state‐of‐the‐art gridless CS algorithm, decoupled atomic norm minimization, is limited to specific 2D array geometries, such as uniform rectangular arrays. This letter presents an efficient gridless 2D DOA estimation algorithm for generalized rectangular arrays, including both uniform and sparse arrays. The proposed algorithm achieves high accuracy through a novel approach called generalized matrix‐form atomic norm minimization and provides a fast solution using the alternating direction method of multipliers. Validation through computer simulations and practical experiments underscores its efficacy. This letter presents an efficient gridless Two‐dimensional direction‐of‐arrival estimation algorithm for generalized rectangular arrays, including both uniform and sparse arrays. By introducing generalized matrix‐form atomic norm minimization, the applicability of atomic norm techniques are extended to a broader range of array geometries. Combining generalized matrix‐form atomic norm minimization with the alternating direction method of multipliers, the approach achieves both high accuracy and computational efficiency.

Traditional polarization-sensitive sensors involve a triplet of spatially collocated, orthogonally oriented, and diversely polarized electric dipoles. However, this kind of sensor has the drawback of severe mutual coupling among the three dipoles due to the characteristic of collocation, as well as low radiation efficiency because of the short length of the dipoles. Based on this problem, in this study we designed a new array structure called a ‘triple coprime array (TCA)’, equipped with long electric dipoles to obtain higher radiation efficiency. In this structure, the dipoles within different subarrays have orthogonal polarization modes, leading to mutual coupling isolation. The dipole interval of the subarrays is enlarged by means of a pairwise coprime relationship, which further weakens the mutual coupling effect and extends the array aperture. Simultaneously, a stable direction-of-arrival (DOA) and polarization estimation method is proposed. DOA information is accurately refined from the three subarrays without ambiguity problems, with the triple coprime characteristic improving the estimation results. Subsequently, polarization estimates can be obtained using the reconstructed model matrix and the least squares method. Numerous theoretical analyses were conducted and extensive simulation results verified the advantages of the TCA structure in mutual coupling, along with the superiority of the proposed joint DOA and polarization estimation algorithm in terms of estimation accuracy.

Metasurfaces, interacted with artificial intelligence, have now been motivating many contemporary research studies to revisit established fields, e.g., direction of arrival (DOA) estimation. Conventional DOA estimation techniques typically necessitate bulky-sized beam-scanning equipment for signal acquisition or complicated reconstruction algorithms for data postprocessing, making them ineffective for detection. In this article, we propose a machine-learning-enabled metasurface for DOA estimation. For certain incident signals, a tunable metasurface is controlled in sequence, generating a series of field intensities at the single receiving probe. The perceived data are subsequently processed by a pretrained random forest model to access the incident angle. As an illustrative example, we experimentally demonstrate a high-accuracy intelligent DOA estimation approach for a wide range of incident angles and achieve more than 95% accuracy with an error of less than . The reported strategy opens a feasible route for intelligent DOA detection in full space and wide band. Moreover, it will provide breakthrough inspiration for traditional applications incorporating time-saving and equipment-simplified majorization.