Intelligent reflecting surfaces (IRS) enable radar sensing in blocked environments by reconfiguring propagation and creating virtual apertures, which is crucial for non-line-of-sight (NLoS) localization. This work addresses high-accuracy direction-of-arrival (DoA) estimation in semi-passive IRS-assisted sensing. We introduce a virtual-domain lifting that vectorizes the received echoes and induces a structured atomic set, leading to an atomic norm minimization (ANM) formulation. The ANM estimator is formulated as a semidefinite program (SDP) via convex relaxation, and we develop an accelerated proximal gradient (APG) solver that leverages the problem structure and avoids interior-point steps, resulting in substantial computational savings. Compared with spatial-domain and grid-based approaches, the transformed-domain estimator delivers an optimal accuracy-complexity tradeoff. It achieves gridless (ANM-level) high resolution while reducing runtime. Extensive simulations across array sizes, transmit power, and IRS configurations confirm accuracy, robustness to off-grid mismatch, and scalability, demonstrating the practicality of the proposed method for IRS-enabled NLoS sensing in complex environments.

With the rapid development of autonomous unmanned systems, the Internet of unmanned agents (IUAs) has emerged as a prominent research field. Direction of arrival (DOA) estimation enables intelligent base stations (IBSs) to detect the direction of unmanned device, facilitating essential functions such as target localization and tracking, and cooperative navigation, significantly enhancing the environmental perception capabilities and task execution efficiency of IUA systems. However, the traditional DOA estimation algorithms in practical complex electromagnetic mutual coupling environments are computationally intensive, incompatible with IUA’s high real-time requirements. To address these challenges, this article proposes a data-driven (DD) DOA estimation method for unmanned device localization in IUA. The IUA localization system comprises four IBS equipped with uniform linear arrays (ULAs). Device localization is achieved through DOA estimates from these four IBS. A deep learning (DL) is proposed to jointly address two key challenges: 1) IUA’s demand for real-time algorithm performance and 2) mutual coupling effects between IBS sensors. A novel DL architecture is designed to estimate off-grid angle parameters and mutual coupling coefficients. The framework incorporates two learnable modules, one focusing on mutual coupling coefficients and another aimed at precise DOA estimation and associated confidence levels. The target unmanned device position is estimated using the least squares method applied to the DOA measurements from all IBSs. The proposed DL-based algorithm surpasses existing methods while maintaining low computational complexity. Extensive simulations demonstrate the high performance and real-time capabilities of the DD-based solution.

Deep Learning (DL) achieves significant performance in estimating the direction of arrival (DOA) in array signal processing. However, many existing DL methods require a large amount of data to train a specialized DL network. To reduce data requirements for training, this paper presents a novel DL-based DOA estimation algorithm for limited training data(LTDDOA-net). The proposed algorithm utilizes the properties of second-order derivatives of the loss function and the ‘learn to learn’ approach to construct a framework that can achieve good performance with minimal data training. Initially, we developed a neural network designed for DOA estimation. This network was subsequently trained using proposed method and loss function on a limited dataset. Ultimately, we validated the practicality and benefits of our approach through simulations and hardware experiment. The results of simulations and hardware experiment have verified the superiority of the proposed approach.

This article explores the integration of Reconfigurable Intelligent Surfaces (RIS) with Frequency Diversity Array (FDA) radars to advance sixth-generation (6G) communication technologies. The study specifically addresses the challenge of low-cost RIS-assisted FDA radar localization. To solve the joint angle-range estimation problem, we employ the Atomic Norm Minimization (ANM) approach. Traditional semidefinite programming (SDP) methods, often used for this purpose, suffer from high complexity and dependence on interior point methods. To improve efficiency, we propose an iterative solution based on the Alternating Direction Method of Multipliers (ADMM). Our simulation results demonstrate that this ADMM-based method not only enhances parameter estimation accuracy but also maintains low computational complexity, outperforming existing algorithms. This research marks a significant step forward in radar localization technology, effectively combining RIS with FDA radars and introducing a novel, efficient method for precise signal processing.

As the safety problems and economic losses caused by traffic accidents are becoming more and more serious, intelligent transportation system (ITS) came into being. After the outbreak of COVID-19, how to achieve effective traffic scheduling and macro command under less contact has attracted more attention. Therefore, the location estimation of traffic objectives is a key issue. In the developed framework, for the target parameter estimation in traffic, frequency diversity array multiple-input multiple-output (FDA-MIMO) radar is introduced into ITS, and tensor decomposition is used to process transportation big data (TBD) to improve the real-time performance of target location estimation. Unfortunately, spatial colored noise and array gain-phase error will affect the performance of FDA-MIMO radar in ITS. An algorithm that can solve the angle-range estimation problem of FDA-MIMO radar in the co-existence of array gain-phase error and spatial colored noise is proposed. Firstly, the four-dimensional tensor is constructed by using the temporal un-correlation of colored noise. Therefore, the influence of colored noise in ITS is removed. Secondly, the direction matrix containing target information is obtained by parallel factor (PARAFAC) decomposition. For the array gain-phase error, the optimization problem is constructed, and the Lagrange multiplier is employed to calculate the optimal solution. The effect of gain-phase error is eliminated by utilizing the optimal solution and the direction matrices. Finally, the location information of motor vehicle is achieved by calculating the solution of least square (LS) fitting. The developed scheme can achieve the location information of motor vehicles in the co-existence of array gain-phase error and spatial colored noise. Comprehensive numerical experiments illustrate that the developed scheme in ITS can efficiently obtain the location information of motor vehicles.