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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. This paper proposes a motion‐based synthetic array framework with error correction tailored for unmanned aerial vehicle (UAV) platforms, along with a multidimensional error calibration method. Experimental results demonstrate that the proposed approach enhances the array aperture (AA) and degrees of freedom (DOF), while enabling highly accurate and robust direction of arrival (DOA) estimation. This study offers an effective solution for airborne moving array direction finding.

This letter proposes a direction of arrival (DOA) and time of delay (TOA) joint estimation algorithm with deep transfer learning. Recently deep learning technique has been applied to solve the joint estimation problem by using the pretrained network and perform well. But in real applications, different scenarios require to cost much time to obtain different pretrained network. In order to overcome these problems, a transfer scheme for DOA and TOA joint estimation is proposed based on a multi‐task network, which uses a shared‐private structure to enhance the transferability of the pretrained network in different signal‐to‐noise ratio (SNR) scenarios. Thus, for different target scenarios, the proposed transferring scheme just uses a few of data from new scenario to fine‐tune pretrained network, which can effectively reduce the computation complexity with satisfied estimation accuracy. Simulation results show that the proposed algorithm is superior to other traditional methods in estimation accuracy and efficiency under different SNR testing scenarios. This article proposed direction of arrival and time of delay joint estimation algorithm with deep transfer method. Proposed method can promote AI development and application in indoors position and outdoors localization.

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.

Sparse Bayesian learning (SBL) is widely applied in direction‐of‐arrival (DOA) estimation. However, it is limited by complexity, grid mismatch and inappropriate initial values of noise. To address these problems, a DOA estimation algorithm using grid pruning and adaptation based on noise integral‐based sparse Bayesian learning (AGNISBL) method is proposed in this letter. To reduce complexity, grid pruning is introduced for expectation maximization (EM) framework. Furthermore, a novel adaptive‐grid method is proposed for solving grid mismatch. A noise integral‐based inference framework is used to improve the robustness of the sparse Bayesian method. Simulation results show that the performance of the proposed AGNISBL approaches CRB at high signal‐to‐noise ratios (SNR) and with lower time complexity compared to other methods. This paper proposes a DOA estimation algorithm using grid pruning and adaptation based on noise integral‐based sparse Bayesian learning (AGNISBL) method. It addresses the traditional SBL method high complexity, grid mismatch and inappropriate initial values of noise problems. Simulation results show that the performance of proposed AGNISBL approaches CRB at high signal‐to‐noise ratios (SNR) and with lower time complexity compared to other methods.

Under low‐snapshot conditions, traditional direction‐of‐arrival (DOA) estimation suffers from covariance instability, while existing deep learning methods rely on complex architectures. This letter proposes a hybrid approach that combines model‐driven and data‐driven theories to strike a balance between estimation performance and computational cost. We reconstruct a structured covariance matrix by applying adaptive diagonal loading. The reconstructed matrix is then transformed into a two‐channel input and fed into the proposed squeeze‐and‐excitation multi‐scale deep convolutional network (SE‐MSDCN). DOA estimates are obtained via a sub‐grid peak interpolation strategy. The experimental results and our analysis validate the efficiency and superiority of the proposed method. This letter proposes a hybrid approach that combines model‐driven and data‐driven theories to strike a balance between estimation performance and computational cost. We reconstruct a structured covariance matrix by applying adaptive diagonal loading. The reconstructed matrix is then fed into the proposed squeeze‐and‐excitation multi‐scale deep convolutional network (SE‐MSDCN) to improve the performance of DOA estimation.

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.

A general challenge in drone localisation and navigation is the strong dependence on satellite positioning. Reconfigurable intelligent surfaces (RIS) offer a promising technology for global navigation satellite system (GNSS)‐free drone localisation and for the alleviation of the jammer effect by nulling the jamming signal. In this letter, we investigate how to optimise the nullification of the jammer while maximising the desired signal in a double RIS‐aided GNSS‐free drone localisation setup. The main objectives of the presented procedure are the maximisation of the desired signal received by the drone, minimisation of the jamming signal effect, accurate estimation of the angle of departure (AOD) from RIS to drone, and drone positioning in 3D. To address this challenge, we propose an alternating projection algorithm to perform jamming nullification by optimising the phase shift at each RIS element. Simulations show that by proper RIS element phase optimisation, the effect of the jammer is efficiently reduced, the AODs from RIS surfaces to drone are computed more accurately, and the drone 3D localisation accuracy is improved. This letter explores a double reconfigurable intelligent surfaces (RIS)‐aided global navigation satellite system‐free drone localisation setup to optimise jammer nullification while enhancing the desired signal. We propose an alternating projection algorithm to optimise the RIS phase shifts, achieving efficient jammer suppression and improved 3D drone positioning. Simulation results demonstrate that the proposed approach significantly enhances localisation accuracy and mitigates jamming effects.