Integrating GNSS in UAVs for faster SAR

31/07/2017

The main objective of the Horizon 2020-funded MOBNET project is to locate victims during natural disasters and emergency situations such as earthquakes, hurricanes or large snowstorms using EGNSS (both Galileo and EGNOS) and DCT (Digital Cellular Technologies). Its system assumptions were presented at TRANSCOM 2017 in Slovakia at the start of June.

Unmanned aerial vehicles (UAVs) are playing an MOBNET is designing a technologically advanced SAR system to locate victims in the event of an emergency.increasingly important role in Public Protection and Disaster Relief (PPDR) missions such as border surveillance and law enforcement. However, quickly locating isolated individuals in the event of a natural or man-made disaster still poses a significant challenge. Consequently, there is a need for an effective system for people location that can be used by PPDR services in difficult terrain.

Tailor-made solution

In response to this need, MOBNET is designing a technologically advanced Search and Rescue (SAR) system that will help to locate isolated victims in the event of an emergency. A concept for a UAV was created based on lessons learned from past experience and on the results of a survey conducted among targeted users. This will ensure that the project delivers a solution that is tailor-made to their needs. This survey is ongoing; to participate click here.

MOBNET is developing a solution to these challenges by leveraging:

  • Mobile phones’ ubiquity in today’s society. In 2016, mobile phone penetration had reached 99.7% worldwide, rising to 126.9% in developed countries;
  • The high-quality timing synchronisation capabilities that Galileo provides worldwide and EGNOS provides in European countries, which means that the UAVs will be positioned with high precision;
  • The cost and performance gains of SAR operations using UAVs. With no pilot on board, UAVs can enter dangerous environments, can stay in the air for long periods with the same reliability, and can perform various types of analysis.

Taking advantage of these three features, MOBNET uses DCT to detect the presence of people (locating their mobiles) and help rescuers in their search. Moreover, the use of EGNOS and Galileo services allows the system to accurately position the UAVs and time tag the ranging estimates with high accuracy, so that MOBNET is able to quickly find the trapped person.

MOBNET combines observations from several drones, each equipped with an EGNSS module and a new DCT module. The drones, flying over the area of interest, use the MOBNET DCT module to detect the victims: i.e. the signals from their mobile phones are used to detect the position of a possible victim. The on-board EGNSS module provides accurate position and time information. MOBNET benefits from the high level of accuracy of the time reference that Galileo satellites provide.

Used in Unmanned Aerial Vehicles (UAVs), the EGNSS information makes accurate positioning possible in any kind of terrain, which makes the system very useful for first responders and other targeted users in situations in which it is difficult, dangerous or even impossible to access the affected areas. A great advantage of the system is that it can help save people’s lives without risking the integrity and security of the first responders’ services.

User-driven

Research is driven by the end-user and industrial partners to ensure that it addresses the needs of the PPDR services. A prototype will be developed to illustrate the potential for a fast and reliable SAR system that works at long distances. The developed system will leverage Galileo and EGNOS capabilities and will strengthen the position of European industry in the field of rescue services.

The solution does not intend to replace traditional methods used by search and rescue teams, such as rescue dogs, geophones and specialised cameras, but to support these activities and to maximise the probability of successfully locating victims. The system will be tested in field conditions in November 2017.

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