Uncovering GNSS Interference with Aerial Mapping UAV
- URL: http://arxiv.org/abs/2405.07611v1
- Date: Mon, 13 May 2024 10:21:03 GMT
- Title: Uncovering GNSS Interference with Aerial Mapping UAV
- Authors: Marco Spanghero, Filip Geib, Ronny Panier, Panos Papadimitratos,
- Abstract summary: We propose a method that combines advanced flight dynamics with high-performance consumer receivers to detect interference over large areas.
The proposed system can detect interference sources and map their area of influence, gaining situational awareness of poor quality or denied environments.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Global Navigation Satellite System (GNSS) receivers provide ubiquitous and precise position, navigation, and time (PNT) to a wide gamut of civilian and tactical infrastructures and devices. Due to the low GNSS received signal power, even low-power radiofrequency interference (RFI) sources are a serious threat to the GNSS integrity and availability. Nonetheless, RFI source localization is paramount yet hard, especially over large areas. Methods based on multi-rotor unmanned aerial vehicles (UAV) exist but are often limited by hovering time, and require specific antenna and detectors. In comparison, fixed-wing planes allow longer missions but are more complex to operate and deploy. A vertical take-off and landing (VTOL) UAV combines the positive aspects of both platforms: high maneuverability, and long mission time and, jointly with highly integrated control systems, simple operation and deployment. Building upon the flexibility allowed by such a platform, we propose a method that combines advanced flight dynamics with high-performance consumer receivers to detect interference over large areas, with minimal interaction with the operator. The proposed system can detect multiple interference sources and map their area of influence, gaining situational awareness of poor GNSS quality or denied environments. Furthermore, it can estimate the relative heading and position of the interference source within tens of meters. The proposed method is validated with real-life measurements, successfully mapping two interference-affected areas and exposing radio equipment causing involuntary in-band interference.
Related papers
- Consumer INS Coupled with Carrier Phase Measurements for GNSS Spoofing Detection [0.0]
Inertial Measurement Units have proved successful in augmenting the accuracy and robustness of the provided navigation solution.
But effective navigation based on inertial techniques in denied contexts requires high-end sensors.
We show that simple MEMS INS perform as well as high-end industrial-grade sensors.
arXiv Detail & Related papers (2025-02-06T08:34:23Z) - Low-altitude Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning [72.72954660774002]
Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas.
Extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks.
This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle.
arXiv Detail & Related papers (2025-01-26T10:13:51Z) - Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization [2.469551405169408]
Large language models (LLMs) are advanced AI systems applied across various domains, including NLP, information retrieval, and recommendation systems.
interference monitoring is essential to ensure the reliability of vehicle localization on roads.
Our pipeline outperforms state-of-the-art machine learning models in interference classification tasks.
arXiv Detail & Related papers (2025-01-09T09:01:04Z) - Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue
with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems.
This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
arXiv Detail & Related papers (2023-08-14T16:48:57Z) - UAV-aided RF Mapping for Sensing and Connectivity in Wireless Networks [52.14281905671453]
The use of unmanned aerial vehicles (UAV) as flying radio access network (RAN) nodes offers a promising complement to traditional fixed terrestrial deployments.
Radio mapping is one of the challenges related to this task, referred here as radio mapping.
The advantages induced by radio-mapping in terms of connectivity, sensing, and localization performance are illustrated.
arXiv Detail & Related papers (2022-05-06T16:16:08Z) - A Multi-UAV System for Exploration and Target Finding in Cluttered and
GPS-Denied Environments [68.31522961125589]
We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles.
The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map.
Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
arXiv Detail & Related papers (2021-07-19T12:54:04Z) - Learning-Based UAV Trajectory Optimization with Collision Avoidance and
Connectivity Constraints [0.0]
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks.
In this paper, we reformulate the multi-UAV trajectory optimization problem with collision avoidance and wireless connectivity constraints.
We propose a decentralized deep reinforcement learning approach to solve the problem.
arXiv Detail & Related papers (2021-04-03T22:22:20Z) - Integrating LEO Satellites and Multi-UAV Reinforcement Learning for
Hybrid FSO/RF Non-Terrestrial Networks [55.776497048509185]
A mega-constellation of low-altitude earth orbit satellites (SATs) and burgeoning unmanned aerial vehicles (UAVs) are promising enablers for high-speed and long-distance communications in beyond fifth-generation (5G) systems.
We investigate the problem of forwarding packets between two faraway ground terminals through SAT and UAV relays using either millimeter-wave (mmWave) radio-frequency (RF) or free-space optical (FSO) link.
arXiv Detail & Related papers (2020-10-20T09:07:10Z) - Integrating LEO Satellite and UAV Relaying via Reinforcement Learning
for Non-Terrestrial Networks [51.05735925326235]
A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency.
We study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation.
To maximize the end-to-end data rate, the satellite association and HAP location should be optimized.
We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique.
arXiv Detail & Related papers (2020-05-26T05:39:27Z) - Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach [36.587096293618366]
An emerging problem is to track unauthorized small unmanned aerial vehicles (UAVs) hiding behind buildings.
This paper proposes the idea of a dynamic radar network of UAVs for real-time and high-accuracy tracking of malicious targets.
arXiv Detail & Related papers (2020-01-13T23:23:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.