In this letter, we propose a deep learning-based off-grid Direction of Arrival (DOA) estimation method for low Signal-to-Noise Ratio (SNR) scenarios. Specifically, we develop a dual-branch neural network with residual connections that processes frequency-domain features, consisting of a coarse classification branch and a fine regression branch. The classification branch employs a multi-label approach to obtain on-grid results, while the regression branch predicts the residual between the classification outputs and ground-truth angles. This structural design effectively leverages classification results to avoid convergence difficulties associated with direct off grid angle prediction, thereby enhancing DOA estimation accuracy. Simulation results demonstrate that under low SNR conditions, the proposed method outperforms existing approaches, including both classical model-based and other deep learning-based methods.

Massive multiple-input multiple-output (MIMO) array systems are a cornerstone technology for beyond fifth-generation (B5G) and sixth-generation (6G) wireless communications. This paper proposes a novel algorithm for the joint estimation of direction-of-departure (DOD) and direction-of-arrival (DOA) in massive MIMO systems under unknown gain-phase errors. The proposed method first exploits a normalized rotational invariance property to extract the relative amplitude-phase difference vectors between adjacent antenna elements. By incorporating the prior knowledge that the transmitter and receiver phase errors follow a zero-mean distribution, we formulate two decoupled cost functions to enable joint DOD and DOA estimation. We then obtain that the corresponding angular parameters efficiently through low-complexity spectral searches. Notably, the proposed method requires only one well-calibrated transmitter and one well-calibrated receiver, thereby substantially reducing the calibration effort compared with existing approaches. The gain errors are directly estimated from the amplitude-phase difference vectors, while the phase error vectors are reconstructed using the estimated DOD and DOA values. The proposed framework accommodates general conformal transceiver array geometries and effectively mitigates error accumulation in gain-phase calibration. Simulation results verify that the proposed algorithm achieves superior angular estimation accuracy and calibration precision compared with state-of-the-art techniques.

In the evolving landscape of wireless communications, extremely large-scale multiple-input-multiple-output (XL-MIMO) systems offer promising enhancements in capacity and spectral efficiency, particularly in near-field scenarios. This article investigates polar-domain channel estimation methods for near-field wideband XL-MIMO systems, proposing a novel approach based on the bilinear pattern detection (BPD) method. We introduce the multicandidate BPD (MBPD) algorithm, which improves detection accuracy by incorporating adaptive weight matrix adjustments and evaluating multiple candidate modes per iteration. Comprehensive simulations validate the superiority of MBPD over traditional BPD in terms of estimation accuracy and robustness. Furthermore, a detailed complexity analysis demonstrates the computational feasibility of the proposed algorithm. The MBPD algorithm greatly improves polar-domain channel estimation, facilitating more efficient implementations of near-field wideband XL-MIMO systems.

In this letter, a coarray Tensor Train (TT) decomposition method is proposed to locate the targets in bistatic Multiple-Input Multiple-Output (MIMO) radar with uniform planar array (UPA) geometry. Initially, a five-dimensional (5-D) tensor model is established to preserve the multi-dimensional structure in the receive part. Subsequently, a four-dimensional (4-D) low-rank tensor can be obtained by removing redundant elements in the difference coarray of the eight-dimensional (8-D) covariance tensor. The TT-SVD algorithm is then applied to convert this 4-D tensor into a sequential product of lower-order TT-cores. Compared to CANDECOMP/PARAFAC decomposition (CPD) and Tucker decomposition (TD), the advantages of TTD include flexible multi-way data representation and mitigation of the curse of dimensionality. Furthermore, by using the relationship between Vandermonde factors matrices and TT-cores, a new method by combining different TT-cores is devised to estimate the angle parameters. Simulation results confirm the superiority of the proposed TT-aided method over other tensor-based methods for bistatic MIMO radar.

Irregularly spaced arrays are appearing in diverse ares, such as wearable devices, stealth aircrafts. This paper studies the two-dimensional (2D) direction-of-arrival (DOA) estimation issue for an irregularly spaced electromagnetic vector sensor (EMVS) array. An estimation method of signal parameters via rotational invariance technique (ESPRIT) approach is developed. Unlike existing ESPRIT-like algorithms, the proposed approach in this paper not only estimates the rough directional cosine waveform via the rotational invariance of the polarized response matrix, but also finds the refined directional cosine waveform via the rotational invariance of the spatial response matrix. This proposed algorithm is capable of offering closed-form analytics, thus greatly facilitating 2D-DOA estimation. Numerical results shown in this paper verify that the proposed approach outperforms existing ESPRIT-like algorithms at a sightly increased costs of computation. In addition, numerical results presented in this paper for the proposed 2D-DOA estimation approach also corroborate the theoretical derivations.

This paper studies super-resolution harmonic retrieval for strictly non-circular coherent signals. We develop gridless sparse representations of both their covariance and pseudo-covariance matrices over a common matrix-form atom set. This enables the decoupled atomic norm minimization (D-ANM) technique to exploit the sparsity of the covariance and pseudo-covariance matrices jointly. Further, by effectively utilizing the inherent mutual coupling characteristics between the covariance and pseudo-covariance matrices, additional constraints are properly imposed to reflect and enforce desired structure information represented by such matrices and their augmented matrix. It leads to a novel structure-based sparse optimization method, called double decoupled atomic norm minimization (DD-ANM). In addition, performance analysis is provided for the proposed DD-ANM method in practical settings. Simulation results reveal that the proposed DD-ANM outperforms the benchmark methods in terms of lower estimation errors.

In the field of Industrial Internet of Things (IIoT) security, Specific Emitter Identification (SEI) plays a crucial role. Recent advancements have seen a rise in the adoption of machine learning (ML) and deep learning (DL) techniques in SEI methodologies, recognized for their impressive effectiveness. However, DL-based SEI methods often incur significant computational costs, making them less suitable for IIoT environments. Similarly, conventional ML-driven SEI approaches depend heavily on feature extraction and employ complex, often redundant classifiers. These methods typically lack in optimizing feature integration and computational efficiency. To overcome these limitations, we introduce an advanced DL-based SEI methodology that focuses on harnessing signal features more effectively. Our method centers around an Adaptive Feature Combination (AFC) strategy, enhanced by an attention mechanism, to develop a more efficient SEI classifier. The essence of our approach is the strategic exploration of adaptive feature combinations, aiming to fine-tune the SEI classifier for peak performance. Simulation results demonstrate that our AFC algorithm outperforms existing SEI methods in both identification accuracy and computational efficiency. This breakthrough offers a viable and promising solution for implementing SEI in IIoT scenarios, achieving heightened effectiveness without sacrificing computational resources.

Direction-of-arrival (DOA), also referred to as angle-of-arrival (AOA), is an excellent choice for unmanned aerial vehicle (UAV) localization and has garnered significant attention recently. In this article, we propose a novel two-dimensional (2D)-DOA auxiliary framework for anonymous UAV localization. At its core, this framework relies on measuring 2D-DOA using a monostatic multiple-input–multiple-output (MIMO) radar configured with electromagnetic vector sensors (EMVSs). Differing from existing mainstream methods, the multipath effect of the UAV is taken into account. A rearrangement multiple signal classification (R-MUSIC) algorithm is developed. The algorithm recovers the covariance matrix rank by connecting spatial responses from both transmitting (Tx) or receiving (Rx) arrays with radar cross-section (RCS) coefficients. Subsequently, rough 2D-DOA estimates are obtained using the vector cross-product (VCP) technique. These rough estimates are then used to establish good initialized values for refined 2-D spectral peak searching. Finally, leveraging the relationship between 2D-DOA and Tx/Rx array coordinates, a UAV’s 3-D position can be directly computed. This framework remains insensitive to the geometric configuration of Tx/Rx arrays while striking a balance between complexity and accuracy. Numerical simulation experiments confirm the improvements of our developed R-MUSIC algorithm.

The increasing significance of unmanned aerial vehicles (UAVs) in mobile edge computing (MEC) has captured considerable attention. Nevertheless, the effectiveness of UAVs-assisted MEC networks is hampered by challenges, such as limited communication capacity and onboard power. To tackle these issues, this study develops a constrained multiobjective optimization model designed to enhance the performance of UAVs-assisted MEC networks, focusing on system capacity, energy consumption, and task latency. As a result, this problem manifests as a complex constrained multiobjective optimization problem. The study then proposes a constrained multiobjective decomposition evolutionary algorithm (CMODEA) with low-computational complexity. This algorithm employs an adaptive individual comparison strategy, balancing diversity and convergence, and integrates an optimally guided differential evolution strategy for efficiently approximating optimal solutions. Additionally, it incorporates an adaptive constraint handling method, effectively managing existing constraints. The CMODEA aims to simultaneously optimize system capacity, energy consumption, and task latency while meeting the computational resource requirements of UAVs and ensuring acceptable user task latency levels. Simulation results demonstrate the algorithm’s effectiveness in significantly enhancing capacity, reducing energy consumption and latency, without greatly increasing algorithm complexity.

Intelligent Reflecting Surface (IRS) represents a significant breakthrough in wireless communications, allowing the reconstruction of wireless channels even for occluded users to the base station (BS). Estimating the Direction-of-Arrival (DOA) of a source oriented toward Non-Line-of-Sight (NLOS) propagation is an intriguing topic in an IRS-aided wireless communication scenario. However, the existing optimization-based approaches are overly complex to be practically implemented. In this paper, we propose a polarized IRS architecture, in which both IRS and BS are equipped with arbitrarily placed Electromagnetic Vector Sensor (EMVS) arrays. A Normalized Vector-Cross Product (NVCP) estimator is developed for DOA estimation, which avoids the need for complicated data recovery or exhaustive grid search. The proposed framework enables Two-Dimensional (2D) DOA estimation for NLOS signals without requiring prior knowledge of the BS-IRS channel. Numerical simulations have been conducted to verify its effectiveness.

The topic of anonymous unmanned aerial vehicle (UAV) localizing based on angle estimation has been frequently discussed in the past few years. However, the existing methodologies are inefficient in a massive sensor arrays scenario. To avoid such drawback, a cooperative 3-D positioning methodology is introduced. The critical idea of the proposed localizing method is to estimate the 2-D angle of the anonymous UAV via a polarized massive–multi-input multi-output (MIMO) system. To reduce the computational burden and explore the nature of the multidimensional data, a tensor compressive sampling (TCS) framework is proposed. Moreover, a closed-form estimation strategy is developed for 2-D direction finding. Our framework is shown to be more efficient than the existing algorithm in terms of hardware/software complexity. Besides, it is suitable for a polarized MIMO system with an arbitrary array geometry. Several simulation examples are provided to show its improvement of the new methodology.

The Angle-of-Arrival (AoA)-based approach is an appealing solution for unmanned aerial vehicle (UAV) positioning, and has received significant interest recently. In this article, we propose a novel framework for UAV three-dimensional (3-D) positioning, the core of which is to measure the two-dimensional (2-D) Angle-of-Departure (2D-AoD) and 2D-AoA via a bistatic multiple-input multiple-output (MIMO) radar. Unlike the existing positioning architectures, the MIMO radar is equipped with polarized array antennas. An estimator based on the parallel factor (PARAFAC) decomposition is developed. It first obtains the direction matrices via performing the PARAFAC decomposition of the array data. Thereafter, the rotational invariance characteristic is utilized to form a normalized polarization response vector, from which the 2D-AoD, 2D-AoA, and polarization status of the UAVs are achieved via incorporating the vector cross-product method and the least squares (LSs) technique. Finally, the 3-D positions of the UAVs are easily calculated via the location relationship between the 2D-AoD, 2D-AoA, and the coordinates of transmitting/receiving (Tx/Rx) array. The proposed framework is computationally friendly, and is capable of positioning anonymous UAV. Moreover, it is insensitive to the geometry of the Tx/Rx array, indicating that the proposed framework supports configurable Tx/Rx antennas. Simulation results are provided to verify our theoretical advantages.

This paper aims to estimate the two dimensional (2D) direction-of-arrival (DOA) using a polarized uniform rectangular array (URA) under multipath propagation. To leverage the tensorial nature, a parallel factor (PARAFAC) model is established, in which it comprises two spatial response matrices, the polarization response matrix, and the source matrix. Unfortunately, the source matrix exhibits rank-deficiency, hindering effectively PARAFAC decomposition. Our analysis reveals that the rank-deficiency can be easily resolved by taking the KhatriRao product with a full column rank factor matrix. Consequently, three rearranged PARAFAC tensors are obtained that are free of the source matrix's rank-deficiency. The estimation of 2D-DOA is then performed using the vector cross product-auxiliary rotational invariance technique (VCPARIT). The proposed algorithms are insensitive to inter-sensor distance and are suitable for a one-snapshot scenario. Furthermore, they outperform existing smoothing methods from the perspective of estimation accuracy. Theoretical advantages of the proposed algorithms are corroborated by the simulations.

The polarized massive multiple-input multiple-output (MIMO) technique has been regarded as a promising solution to millimeter wave (mmWave) communication systems, because it experiences more degrees-of-freedom than the scalar configuration, and it represents a significant opportunity for secure communication. To deliver smart service to terminals, it is essential to provide base stations (BS) with the capability of terminal’s direction-of-arrival (DOA) awareness. In this paper, a compressive sampling (CS) framework is proposed for two-dimensional (2D) DOA and polarization estimation in mmWave polarized massive MIMO systems. The proposed approach first reduces the data volume via a reduced-dimension matrix. Then it computes the signal subspace via the eigendecomposition of the compressed array measurement. Thereafter, the rotational invariance characteristic is utilized to form a normalized polarization steering vector. Finally, 2D-DOA and polarization are estimated by incorporating the Poynting vector and the least squares (LS) techniques. The proposed architecture is computationally much more economical than existing algorithms. Besides, it allows a mmWave BS to provide comparable estimation performance with arbitrary sensor geometry, which is more flexible than most of the existing architectures. Furthermore, it is robust to the sensor position error. Numerical simulations verify the advantages of the proposed framework.

This paper revisits the problem of multiple parameters estimation for coherent targets in bistatic EMVS-MIMO radar. By extending the spatial smoothing mechanism to the EMVS-MIMO radar, three generalized spatial smoothing estimators, named the TS approach, the RS approach and the TRS approach, have been proposed. The killer idea of the proposed methodologies is to recover the rank of the covariance matrix via averaging the array measurement in spatial domain, and then estimate the parameters from the cooperation of the normalized vector cross-product technique and the LS method. Unlike the state-of-the-art polarization smoothing methods, the proposed methods would not sacrifice the polarization information, so that TS, RS and TRS are capable of providing polarization information of the coherent targets. The proposed estimators do not require any constrain on the geometries of the Tx/Rx EMVS arrays. They are computationally friendly yet retain robustness to the sensor position error. The proposed estimators are analyzed in detail, and numerical simulations are provided to verify their theoretical advantages.

Owing to the large degrees of freedom and reduced mutual coupling by producing difference coarrays, nonuniform linear arrays have aroused great interest in direction of arrival (DOA) estimation. Previous works have presented some new sparse arrays, such as the thinned coprime array. In this paper, we propose a generalized thinned coprime array by introducing the flexible inter-element spacings, where the conventional one can be seen as a special case. We derive closedform expression for the range of consecutive lags, written as the functions of the antenna numbers and inter-element spacings. We show that, after optimization, the proposed array can achieve more consecutive lags than the other coprime arrays. In particular, the optimized results also provide the minimum number of antenna pairs with small separation. Simulation results demonstrate the superiority of the proposed GTCA using the subspace-based method.

Diversity smoothing has been widely developed for angle estimation with bistatic multiple input multiple output (MIMO) radar in the presence of coherent targets, the parameter identifiability of which is an important issue. In this paper, we are devoted to establishing more accurate conditions by studying the positive definiteness of smoothed target covariance matrix. The antenna numbers of transmit and receive arrays are derived as functions of the target number and target structure. We show that the new results improve upon previous ones and recover them in special cases. Simulation results are presented that corroborate our theoretical findings.

Electromagnetic vector sensor (EVS) array has drawn extensive attention in the past decades, since it offers two‐dimensional direction‐of‐arrival (2D‐DOA) estimation and additional polarization information of the incoming source. Most of the existing works concerning EVS array are focused on parameter estimation with special array architecture, e.g., uniform manifold and sparse arrays. In this paper, we consider a more general scenario that EVS array is distributed in an arbitrary geometry, and a novel estimator is proposed. Firstly, the covariance tensor model is established, which can make full use of the multidimensional structure of the array measurement. Then, the higher‐order singular value decomposition (HOSVD) is adopted to obtain a more accurate signal subspace. Thereafter, a novel rotation invariant relation is exploited to construct a normalized Poynting vector, and the vector cross‐product technique is utilized to estimate the 2D‐DOA. Based on the previous obtained 2D‐DOA, the polarization parameter can be easily achieved via the least squares method. The proposed method is suitable for EVS array with arbitrary geometry, and it is insensitive to the spatially colored noise. Therefore, it is more flexible than the state‐of‐the‐art algorithms. Finally, numerical simulations are carried out to verify the effectiveness of the proposed estimator.

Ideal array responses are often desirable to a multiple‐input multiple‐output (MIMO) system. Unfortunately, it may not be guaranteed in practice as the mutual coupling (MC) effects always exist. Current works concerning MC in the MIMO system only account for the uniform array geometry scenario. In this paper, we generalize the issue of angle estimation and MC self‐calibration in a bistatic MIMO system in the case of arbitrary sensor geometry. The MC effects corresponding to the transmit array and the receive array are modeled by two MC matrices with several distinct entities. Angle estimation is then recast to a linear constrained quadratic problem. Inspired by the MC transformation property, a multiple signal classification‐ (MUSIC‐) like strategy is proposed, which can estimate the direction‐of‐departure (DOD) and direction‐of‐arrival (DOA) via two individual spectrum searches. Thereafter, the MC coefficients are obtained by exploiting the orthogonality between the signal subspace and the noise subspace. The proposed method is suitable for arbitrary sensor geometry. Detailed analyses with respect to computational complexity, identifiability, and Cramer‐Rao bounds (CRBs) are provided. Simulation results validate the effectiveness of the proposed method.

As an important branch of the Internet of Vehicles (IoV), vehicle positioning has drawn extensive attention. Traditional positioning systems based on a global positioning system incur long delays, and may fail due to obstructions. In this article, we propose an auxiliary positioning architecture, whose core is to estimate the direction of arrival (DOA) of signals from landmarks, such as wireless access points, utilizing a sensor array in the vehicle. Due to space limitations, the array may be placed in an arbitrary geometry and may suffer from unknown mutual coupling. Most algorithms are only effective for sensor arrays with special geometries, e.g., a uniform linear array or rectangular array. To tackle this problem, an improved multiple signal classification algorithm is derived, which is superior to the state-of-the-art iterative method from the perspective of computational complexity. Detailed analysis concerning identifiability, computational complexity, and Cramér-Rao bounds are given. The simulation results verify the improvement of the proposed DOA estimation algorithm. The proposed architecture can obtain robust self-localization with existing vehicular ad hoc networks, and it can collaborate with other positioning systems to provide a safe driving environment.

Medical image quality requirements have been increasingly stringent with the recent developments of medical technology. To meet clinical diagnosis needs, an effective medical image enhancement method based on convolutional neural networks (CNNs) and frequency band broadening (FBB) is proposed. Curvelet transform is used to deal with medical data by obtaining the curvelet coefficient in each scale and direction, and the generalised cross‐validation is implemented to select the optimal threshold for performing denoising processing. Meanwhile, the cycle spinning scheme is used to wipe off the visible ringing effects along the edges of medical images. Then, FBB and a new CNN model based on the retinex model are used to improve the processed image resolution. Eventually, pixel‐level fusion is made between two enhanced medical images from CNN and FBB. In the authors’ study, 50 groups of medical magnetic resonance imaging, X‐ray, and computed tomography images in total have been studied. The experimental results indicate that the final enhanced image using the proposed method outperforms other methods. The resolution and the edge details of the processed image are significantly enhanced, providing a more effective and accurate basis for medical workers to diagnose diseases.