Work

Drone Localization Using Dual RIS-Equipped Drones Under Jamming: Joint Jamming Nullification and Localization

Year: 2025

Type: conference-paper

Abstract: Reconfigurable Intelligent Surfaces (RIS) are gaining attention for enhancing localization in Global Navigation Satellite System (GNSS)-denied environments. This paper investigates a single-input single-output (SISO) system for localizing a receiving drone assisted by two mobile RIS-equipped drones while mitigating interference signal from a jamming drone. The RIS drones dynamically steer reflections to enhance signal quality and enable accurate angle-of-departure (AOD) estimation. An adaptive Least Mean Squares (LMS) algorithm is applied to nullify jamming interference and maximize signal-to-interference-plus-noise ratio (SINR).The simulation results demonstrate that the proposed dual-RIS framework significantly improves localization accuracy, even in the presence of a jammer signal. We evaluate the performance of our approach, demonstrating robust AOD estimation and accurate 3D position of the receiving drone, even under severe jamming conditions. These results validate the effectiveness of our method in enhancing localization accuracy in adversarial environments.

Source: IEEE Vehicular Technology Conference

DOI: 10.1109/vtc2025-fall65116.2025.11310403

Authors: Mehari Meles, Akash Rajasekaran, Reino Virrankoski, Riku Jäntti

Institution: Aalto University

Language: English

Topic: Advanced Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: Business Finland