Lane-level autonomous driving relies on high-accuracy vehicle positioning. Among different vehicle positioning approaches, direction of arrival (DOA) based solutions are competitive as they avoid measuring delay information. However, a large distance between the vehicle and road side unit (RSU) compared to the antenna array aperture limits the positioning performance of the existing methods. To address this issue, in this paper, we propose an iterative positioning method using two collaborative RSUs and the spatial geometry, where the DOAs and the positions of vehicles are iteratively estimated. Numerical simulations demonstrate that the proposed method can achieve a positioning performance of millimeter-grade, improving the positioning performance by one to two orders of magnitude, compared to state-of-the-art methods.

Fluid antenna (FA) technology has emerged as a promising approach in wireless communications due to its capability of providing increased degrees of freedom (DoFs) and exceptional design flexibility. This paper addresses the challenge of direction-of-arrival (DOA) estimation for aligned received signals (ARS) and non-aligned received signals (NARS) by designing two specialized uniform FA structures under time-constrained mobility. For ARS scenarios, we propose a fully movable antenna configuration that maximizes the virtual array aperture, whereas for NARS scenarios, we design a structure incorporating a fixed reference antenna to reliably extract phase information from the signal covariance. To overcome the limitations of large virtual arrays and limited sample data inherent in time-varying channels (TVC), we introduce two novel DOA estimation methods: TMRLS-MUSIC for ARS, combining Toeplitz matrix reconstruction (TMR) with linear shrinkage (LS) estimation, and TMR-MUSIC for NARS, utilizing sub-covariance matrices to construct virtual array responses. Both methods employ Nyström approximation to significantly reduce computational complexity while maintaining estimation accuracy. Theoretical analyses and extensive simulation results demonstrate that the proposed methods achieve underdetermined DOA estimation using minimal FA elements, outperform conventional methods in estimation accuracy, and substantially reduce computational complexity.

This paper investigates a design framework for sparse fluid antenna systems (FAS) enabling high-performance direction-of-arrival (DOA) estimation, particularly in challenging millimeter-wave (mmWave) environments. By ingeniously harnessing the mobility of fluid antenna (FA) elements, the proposed architectures achieve an extended range of spatial degrees of freedom (DoFs) compared to conventional fixed-position antenna (FPA) arrays. This innovation not only facilitates the seamless application of super-resolution DOA estimators but also enables robust DOA estimation, accurately localizing more sources than the number of physical antenna elements. We introduce two bespoke FA array structures and mobility strategies tailored to scenarios with aligned and misaligned received signals, respectively, demonstrating a hardware-driven approach to overcoming complexities typically addressed by intricate algorithms. A key contribution is a light-of-sight (LoS)-centric, closed-form DOA estimator, which first employs an eigenvalue-ratio test for precise LoS path number detection, followed by a polynomial root-finding procedure. This method distinctly showcases the unique advantages of FAS by simplifying the estimation process while enhancing accuracy. Numerical results compellingly verify that the proposed FA array designs and estimation techniques yield an extended DoFs range, deliver superior DOA accuracy, and maintain robustness across diverse signal conditions.

Based on the exact spherical wavefront model, an under-determined estimation method for three-dimensional (3-D) parameters of near-field (NF) sources using L-shaped nested arrays is proposed, referred to as the cumulant algorithm. This algorithm leverages the temporal-spatial domain cumulants of NF sources by constructing virtual data through delayed fourth-order cumulant (FOC) calculations of the original received data. Subsequently, a spatial-spectrum-based subspace method is applied for 3-D NF localization, which involves a 3-D spectral search procedure. Additionally, the maximum number of identifiable NF sources of the proposed algorithm is analyzed. Simulation results demonstrate that, based on the exact spherical wavefront model, the proposed algorithm can achieve underdetermined 3-D parameter estimation of NF sources without any matching process, and it performs better in localization than existing methods.

In this paper, a high-precision three-dimensional (3-D) near-field (NF) localization method is proposed under an underdetermined case based on a symmetric enhanced nested array (SENA). Firstly, the symmetry of the array and the fourth-order cumulant (FOC) are utilized to construct the equivalent virtual far-field (FF) reception data. Then, a gridless sparse and parametric approach (SPA), combined with an l1-SVD based pairing procedure, is used to obtain estimates for two paired angles. Finally, a one-dimensional (1-D) spectral estimator is applied to obtain the estimate of range parameter. Simulation results show the effectiveness of the proposed method.

Most existing direction of arrival (DOA) estimation methods are based on antenna arrays for line-of-sight (LOS) propagation. In this article, a different and challenging DOA estimation problem with a single receiving antenna in the non-line-of-sight (NLOS) scenario is addressed, where a reconfigurable intelligent surface (RIS) is combined with two robust array spatial covariance matrix (SCM) reconstruction schemes to solve the problem. In detail, a two-stage approach for high-efficiency RIS phase shifting is first designed, and then the Tikhonov regularization criterion and total least-squares (TLS) criterion are respectively exploited for SCM reconstruction with and without phase shift error (PSE), yielding an improved DOA estimation performance with reduced complexity. Theoretical analysis on the performance of SCM reconstruction is conducted, and simulation results are provided to show the effectiveness of the proposed solutions.

Most of existing direction of arrival (DOA) based vehicle positioning techniques are established on array sample covariance matrix and multiple measurement data, which suffer from severe performance degradation in case of a single snapshot. In this paper, a challenging vehicle positioning scheme based on single-snapshot DOA and impinging signal magnitude-phase estimation is proposed. In detail, DOA is initially estimated by applying the generalized approximate message passing combined with belief propagation (GAMP-BP) algorithm under the assumption of complex discrete random variable with distinct phase information. Depending on the initial DOA estimates, two efficient approaches are respectively investigated for final DOA and signal magnitude-phase estimation, where the refine-grid GAMP combined with the least squares algorithm (GAMP-LS) and the special reweighted sparse total least-squares (SRE-STLS) are respectively adopted. With available DOA and magnitude-phase estimates, a principle for selecting reliable DOA sets is further designed, finally enabling improved vehicle positioning without ambiguity under multiple collaborative road side units (RSUs). Simulations are performed to show the effectiveness of the proposed solution.

In this paper, a deep learning (DL) based approach is investigated for direction-of-arrival (DOA) estimation, where large-scale uniform linear arrays (ULAs) and small number of samples are considered. Different from existing DL based DOA estimators, the proposed solution first exploits the Toeplitz prior of array covariance matrix and the linear shrinkage technique to obtain an enhanced sample covariance matrix (SCM), which is then formulated as a sparse linear representation (SLR) problem. Finally, a suitable deep convolution network (DCN) that learns such a SLR characteristic from large training dataset is designed. With aid of Toeplitz and sparse prior, the proposed solution can provide an increased resolution and estimation accuracy under the considered scenario, as verified by simulations.

Based on the exact source-sensor spatial geometry, a three-dimensional (3-D) spatial-temporal localization algorithm for multiple near-field (NF) sources is proposed without adopting the Fresnel approximation, which simplifies the spatial phase difference by Taylors polynomial. In addition, considering the propagation attenuation which varies from different sensors, the spatial and temporal information can be exploited to construct a third-order parallel factor (PARAFAC) data model and the array manifold matrices can be extracted by trilinear decomposition; then, estimation of the unambiguous range and angle parameters of the NF sources is achieved from the spatial amplitude-phase factors by the least squares method. The obtained 3-D parameters associated with each source require no additional pairing process, as also demonstrated by simulation results.

As an important branch of Internet of Vehicles (IoV) systems, autonomous vehicle (AV) positioning based on direction-of-arrival (DOA) estimation has received extensive attention in recent years. In this article, an AV positioning method under unknown mutual coupling is proposed within the framework of a large-dimensional asymptotic theory (LAT). First, enhanced and closed-form DOA estimation is achieved by jointly exploiting large-scale uniform linear arrays (ULAs), Toeplitz rectification and the phase transformation result associated with the sample covariance matrix; second, a more reliable subset/set of DOAs is constructed according to the signal-to-noise at receivers; finally, robust AV positioning is achieved with the reliable subset/set. Motivated by satisfactory DOA estimation performance, an AV contour extraction scheme is developed with the aid of two antennas installed on an AV. The proposed method shows several salient advantages compared with existing methods, including improved resolution and accuracy, reduced computational complexity, robustness to mutual coupling and unreasonable DOA estimates, as well as the ability to effectively extract AV contour information.

A new near-field source localization method is proposed for two-dimensional (2-D) direction-of-arrival (DOA) and range estimation based on a symmetrical cross array. It first employs the conjugate symmetry property of the signal auto-correlation at different time delays to construct a conjugate augmented spatial-temporal cross correlation matrix, then the extended steering vector is decoupled to avoid the usual multiple-dimensional (M-D) search based on the properties of the Khatri-Rao product, and finally three one-dimensional (1-D) MUSIC type searches are employed to obtain the results. The proposed method can realize automatic pairing of multiple parameters associated with each source and it also works in the underdetermined case.

In this article, a novel vehicle positioning system architecture based on direction-of-arrival (DOA) estimation of incoherently distributed (ID) sources is proposed employing massive multiple-input–multiple-output (MIMO) arrays. Such an architecture with the associated signal model is more consistent with the actual array application and multipath transmission scenarios. First, an end-to-end two-dimensional (2-D) DOA estimation of ID sources utilizing a dual one-dimensional (1-D) convolutional neural network (D1D-CNN) under the deep learning (DL) framework is performed, where the normalized covariance matrix data is used for both offline training and online estimation. Then, the received SNR information is exploited to select a set of DOA estimates provided by multiple collaborative BSs for positioning. Moreover, transfer learning and an attention mechanism are employed to promote its generalization ability and achieve robustness against array perturbations. Simulation results are provided to show that the proposed method outperforms the state-of-the-art methods in terms of computational complexity, positioning accuracy, and robustness against array perturbations.

In massive multiple-input multiple-output (MIMO) systems, accurate direction-of-arrival (DOA) estimation is important for the base station (BS) to perform effective downlink beamforming. So far, there have been few reports on DOA estimation considering gain-phase perturbations in massive MIMO systems. However, gain-phase perturbations indeed exist in practical applications and cannot be ignored. In this paper, an efficient method for two-dimensional (2-D) DOA estimation of incoherently distributed (ID) sources considering array gain-phase perturbations is proposed for massive MIMO systems. Firstly, a shift invariance structure is established in the subspace framework, and a constrained optimization problem is formulated to estimate the nominal azimuth and elevation DOAs as well as gain-phase perturbations with closed-form expressions, under the assumption that some of the BS antennas are well calibrated; secondly, the corresponding angular spreads are obtained with the aid of the estimated gain-phase perturbations. Theoretical analysis and an approximate Cramér-Rao bound are also provided. An improved estimation performance is achieved by the proposed method as demonstrated by numerical simulations.