“We insert all the points along the flight path into a deep neural network that was trained to be able to predict the exact launch point and the location of the drone operator,” Eliyahu Mashhadi of Ben Gurion University says. Testing the model with the flight simulator, the team were able to locate and target the drone operator 78% of the time.
By Arie Egozi on July 10, 2020
The IRGC flew about 50 “offensive and combat” drones in the Persian Gulf, including the Saegheh drone, supposedly based on the American RQ-170. According to sources here, the drone flew for about 1,000 kilometers between designated targets.
TEL AVIV: In March 2019, the Iranian Revolutionary Guards Corps (IRGC) held a drill codenamed “Towards Jerusalem 1,” near the strategic Strait of Hormuz.
Now Israeli scientists are developing a system they believe will let them accurately locate the operator of hostile drones and neutralize him.
Researchers at Ben Gurion University in Beer Sheva in southern Israel, Gera Weiss and Eliyahu Mashhadi, are using a realistic simulation environment to collect the path of the drone when flown from launch point and monitor it its flight path. “We insert all the points along the flight path into a deep neural network that was trained to be able to predict the exact launch point and the location of the drone operator,” Mashhadi said.
Testing the model with the flight simulator, the team were able to locate and target the drone operator 78% of the time.
Today, counter-drone systems use radio frequency to locate the operators, while using electro-optical, radar and other sensors to track the drones. “All the approaches that we are aware of for locating operators, not just the drones, use RF sensors”. Mashhadi explained that there are automatic and semi-automatic methods for locating the operators based on radio communications between the drone and its operator. “There are a number of problems with this approach. Firstly, such methods are usually tailored to a specific brand of drones,” he said. “Furthermore, the radio signal can only be recorded near the drone. Finally, there are ways for malicious drone designers to apply cryptography and electronic warfare techniques to make localization by analysis of radio signals very difficult.”
Mashhadi explained that their experiments show the reactions of the operator due to environmental and physical conditions , give away enough information to obtain substantial information about the location of the operator by analyzing the drone’s path in the sky.
“To allow for a controlled environment, we conducted all our experiments with a flight simulator that provides a realistic flight experience for the operator that includes sun gazes, obstructions, and other visual effects that produce the reactions of the operators that allow us to identify their location,” he said.
The research team used AirSim (Aerial Informatics and Robotics Simulation), an open-source, cross-platform simulator for drones, ground vehicles and other objects.
“The neural network that we have designed was able to take advantage of these relations when we asked it to use only position or only rotation information,” Mashhadi said.
Israeli sources say a system able to find the operator in real time will become critical because, in most cases, the operator is flying more than one drone. There’s other work underway, including an effort by Israel Aerospace Industries (IAI) and an Israeli start-up. They have signed a cooperation agreement for the integration of interception capabilities into IAI’s advanced anti-drone system, Drone Guard. The intercepting drone can be launched day or night from a docking station that hosts several ready-to-use drones. Several intercepting drones can be launched simultaneously to address several targets or swarms.
To date, IAI’s ELTA Systems, which develops and manufactures Drone Guard anti-drone systems, has sold more than 100 units that detect, identify and disrupt the operation of malicious drones. ELTA’s collaboration with Iron Drone is part of its strategy to collaborate with startups to leverage their innovative technologies for their existing systems to improve performance.
The radar detects drones as they enter Israeli airspace and an intercepting drone is launched and steered to the target with the help of the radar. The system uses sensors and computer vision to home and lock on the target. The entire process is autonomous, requiring no human intervention. According to IAI the new joint venture allows customers to react in areas where other defense systems cannot because of environmental factors such as airports, populated areas, power plants, sensitive facilities, and other infrastructures.
Topics: artificial intelligence, autonomy, Ben Gurion University, counter drone, iran, Iranian Revolutionary Guards Corps, Israel, Israel Aerospace Industries IAI, machine learning, RQ-170