Near-Field RIS-Assisted Localization Under Mutual Coupling

Reconfigurable intelligent surfaces (RISs) have the potential to significantly enhance the performance of integrated sensing and communication (ISAC) systems, particularly in line-of-sight (LoS) blockage scenarios. However, as larger RISs are integrated into ISAC systems, mutual coupling (MC) effects between RIS elements become more pronounced, leading to a substantial degradation in performance, especially for localization applications. In this paper, we first conduct a misspecified and standard Cram\'er-Rao bound analysis to quantify the impact of MC on localization performance, demonstrating severe degradations in accuracy, especially when MC is ignored. Building on this, we propose a novel joint user equipment localization and RIS MC parameter estimation (JLMC) method in near-field wireless systems. Our two-stage MC-aware approach outperforms classical methods that neglect MC, significantly improving localization accuracy and overall system performance. Simulation results validate the effectiveness and advantages of the proposed method in realistic scenarios.

Sparsity Based Multi-Source Robust 3D Localization Using a Moving Receiver

Accurately localizing multiple sources is a critical task with various applications in wireless communications, such as emergency services including natural post-disaster search and rescue operations. However, the scenarios where the receiver is moving, are not addressed by recent studies. This paper tackles the angle of arrival (AOA) 3D-localization problem for multiple sparse signal sources with a moving receiver having limited antennas, potentially outnumbered by the sources. First, an energy detector algorithm is proposed to exploit the sparsity of the signal to eliminate the noisy samples of the signals. Subsequently, elevation and azimuth AOAs of sources are roughly estimated using two dimensional multiple signal classification (2D-MUSIC) method. Next, an algorithm is proposed to refine and estimate the elevation and azimuth AOAs more accurately. To this end, we propose a sparse recovery algorithm to exploit the sparsity feature of the signals. Then, we propose a phase smoothing algorithm to refine the estimations in the output of sparse recovery algorithm. Finally, K-SVD algorithm is employed to find the accurate elevation and azimuth AOAs of sources. For localization, a new multi-source 3D-localization algorithm is proposed to estimate the positions of sources in a sequence of time windows. Extensive simulations are carried out to demonstrate the effectiveness of the proposed framework.