RIS-Assisted Three-Dimensional Drone Localization and Tracking Under Hardware Impairments

Abstract

The concept of Reconfigurable Intelligent Surfaces (RIS) has emerged as a promising method for communications and the localization of aeronautical vehicles. In this paper, we explore the impact of hardware impairments on three-dimensional (3D) drone localization within a single-input single-output (SISO) system assisted by RIS. Our methodology begins by modeling the channel from the base station (BS), equipped with a single-antenna transmitter, to each RIS at known positions. This model accounts for hardware impairments at the BS, particularly beam downtilt, which influences the accuracy of drone location estimation. Moreover, we model the channel from the RIS to the drone, employing exhaustive beam sweeping in both azimuth and elevation angles to estimate the Angles of Departure (AODs) from the RIS to the drone. We adopt a unique phase noise (PN) model for each element within the RIS and assess the impact of these impairments on angle and location estimation accuracy through extensive simulations. Additionally, we examine the effects of RIS configuration and the Inter-Site Distance (ISD) between two RIS units on localization performance. An Unscented Kalman Filter (UKF) algorithm is integrated for tracking of the drone trajectory. Our simulation results demonstrate that the RIS-assisted 3D drone localization approach achieves significant accuracy despite various impairments. The findings of this paper underscore the potential of RIS-enabled 3D drone localization to maintain high accuracy under hardware impairments, paving the way for future research in RIS-enabled drone localization systems.

Keywords

• AOD
• localization
• RIS
• UKF
• drone