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Abstract
The rapid development of wireless systems presents unprecedented challenges and opportunities for mobile radio localization. On the one hand, the demand for precise and reliable localization capabilities in 5G/6G systems is growing. On the other hand, emerging technologies such as reconfigurable intelligent surfaces (RISs) and low Earth orbit (LEO) satellites are revolutionizing radio localization techniques. Therefore, new signal processing solutions are imperative for radio localization in next-generation wireless systems.
Initially, we investigate RIS-aided radio localization. As a game-changer, RIS can intelligently reshape the radio environment and substantially benefit localization. In RIS-aided localization, prior information on the position and orientation of RIS itself is crucial. We first demonstrate through a misspecified Cramér-Rao bound (MCRB) analysis the impact of RIS geometry mismatches on RIS-aided localization, revealing the importance of RIS geometric calibration. Then, we propose a joint RIS calibration and user positioning (JrCUP) method based on the tensor-ESPRIT and least-squares principles.
Next, we explore global navigation satellite system (GNSS)-enhanced radio localization. Although new localization technologies are emerging, GNSS remains the most prevalent one, thanks to its global coverage, low cost, and all-weather availability. In this part, we investigate the hybrid localization problem and solution integrating 5G radio and GNSS observations, demonstrating that the cooperation between these two technologies can significantly benefit each other and achieve mutual enhancement.
Finally, we study an integrated terrestrial and non-terrestrial wireless network that coexists with LEO satellites and RISs, addressing the dynamic user tracking problem beyond snapshot localization. By jointly leveraging user dynamics and channel observations, we propose a user tracking algorithm that coordinates LEO satellites and ground-based RISs. Utilizing Riemannian manifold theory and the unscented Kalman filter, the proposed method can robustly track the user's position, velocity, and orientation across diverse environments.
Brief Biography
Pinjun Zheng received the B.S. and M.S. degrees from the Harbin Institute of Technology, Harbin, China, in 2019 and 2021, respectively. He is currently pursuing the Ph.D. degree in electrical and computer engineering (ECE) at the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. His research interests include signal processing and estimation theory, with a particular focus on applications in radio localization and communication.
