English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Reconfigurable intelligent surface (RIS)-aided localization systems areincreasingly recognized for enhancing accuracy in internet of things (IoT)networks. However, prevailing studies tend to either assume a Gaussiandistribution for angle estimation error (AEE) or directly neglect the impact ofthe AEE, overlooking its non-Gaussian nature in real-world scenarios,particularly with diverse estimation methods (e.g., 2D-DFT algorithm).Addressing this oversight, this paper explores the design and performanceanalysis of RIS-aided localization systems, specifically tackling non-GaussianAEE. We adopt the classical two-step three-dimensional (3D) localization schemeto determine the position of mobile user (MU). Initially, we estimate angles ofarrival (AoAs) and time differences of arrival (TDoAs) at the RIS usingdifferent methods, resulting in non-Gaussian and Gaussian errors, respectively.Subsequently, to accommodate the non-Gaussian nature of AoAs errors and theGaussian character of TDoA errors, we design a multiple weighted least squares(mWLS) algorithm to accurately localize MU. Besides, our research also includesa unique bias analysis for evaluating the performance of the proposedlocalization algorithm under both Gaussian and non-Gaussian errors. Simulationresults demonstrate the effectiveness of both the proposed mWLS algorithm andthe bias analysis methodology.\end{abstract}

RIS-Aided Localization Algorithm and Analysis: Tackling Non-Gaussian Angle Estimation Errors