Accurate localization is fundamental for internet of things (IoT) applications but introduces significant privacy risks in crowdsourcing-based systems. Conventional privacy-preserving techniques suffer from critical limitations: subtraction-based models sacrifice positioning accuracy by discarding measurement equations; cryptographic solutions incur prohibitive computation overhead; while secret-sharing approaches expose target locations. To address these challenges, we propose the mean-based subtraction localization (MSL) model that preserves all measurement equations through global averaging, eliminating information loss while establishing a privacy-conducive mathematical framework. Building on MSL, we develop the mean-based privacy-preserving localization (MPPL) algorithm featuring: 1) matrix decomposition for bidirectional privacy isolation, 2) lightweight secure summation/multiplication primitives, and 3) linear-complexity operations. Rigorous theoretical verification confirms MPPL’s feasibility, correctness, information-theoretic privacy, and efficiency. Experimental results demonstrate 10% – 14% higher accuracy than privacy-preserving multi-lateral localization (PPPL)/efficient privacy-preserving localization (EPPL) under noise while reducing computation time by a factor of 2.8 versus homomorphic encryption schemes, establishing a new paradigm for high-precision privacy-aware localization.

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.

Direction-of-arrival (DOA) estimation for incoherently distributed (ID) sources is crucial for Industrial Internet of Things (IIoT) applications operating in complex multipath environments, yet it remains challenging due to the combined effects of angular spread and gain-phase uncertainties in cost-sensitive antenna arrays. This paper presents a two-stage sparse DOA estimation framework, transitioning from partial calibration to full potential, under the generalized array manifold (GAM) framework. In the first stage, coarse DOA estimates are obtained by exploiting the output from a subset of partly-calibrated arrays (PCAs). In the second stage, these estimates are utilized to determine and compensate for gain-phase uncertainties across all array elements. Then a sparse total least-squares optimization problem is formulated and solved via alternating descent to refine the DOA estimates. Simulation results demonstrate that the proposed method achieves superior estimation accuracy compared to existing approaches, while maintaining robustness against both noise and angular spread effects in practical industrial environments.

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.

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.

Attaining centimeter-level vehicle positioning is a fundamental requirement for lane-level autonomous driving. Pursuing this objective from the perspective of direction-of-arrival (DOA) estimation is promising, which has emerged as a prominent research topic. In order to simultaneously meet the demands of low complexity and high accuracy, it is crucial for DOA-based solutions to address pressing challenges, including the model mismatch problem and reliable positioning in scenarios with limited samples. This article explores a novel vehicle positioning scheme employing DOAs obtained from collaborative base stations (BSs) or roadside unit (RSU). To cope with the actual propagation scenarios and avoid nonrandom systematic error, the exact spatial geometry (ESG) for DOA estimation is adopted. Under the ESG model, a two-stage unitary approximate message passing (UAMP)-based DOA estimation method is proposed. With DOAs estimated at multiple collaborative BSs/RSUs, the locations of vehicles are finally obtained with cross-localization criterion. Numerical simulations are provided to show that the proposed method is effective and delivers competitive performance. Furthermore, inspired by intriguing simulation results, we design a DOA subset selection mechanism that enhances the reliability of positioning performance.

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.

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.