The general overall availability and the ability to tweak drones has heightened security implications for critical infrastructure such as power plants, sports stadiums and correctional facilities.
Traditional detection technologies, such as radar, have proven somewhat ineffective at identifying drones due to their small size and low flight altitude.
Blending his previous work in AI and wireless communication, Wang’s drone detection intervention includes an antenna to capture radio frequency signals transmitted between the drone and its operator, enabling the system to detect and estimate the locations of both.
Collecting Signatures
Because there are dozens of different types of drones, Wang and his team must first collect each type’s signal “signature.” The second component of the process includes an artificial intelligence algorithm that can detect, classify and localize the drone in the sky.
“Every week, we are going to fly different types of drones on campus in order to collect their corresponding data,” Wang said.
That dataset will be widely available for other researchers, he said.
“We want to share the full dataset we have collected at the Rowan campus and let everyone utilize the data we have collected to boost this kind of drone defense research,” Wang said.
Huaxia Wang, Ph.D., and assistant professor at Rowan University is leading a team to establish new drone detection systems.
Source: Rowan University
Wang is partnering with Ziang Gao, who leads the R&D team at New Jersey-based drone detection company AeroDefense, to advance and commercialize the technology. Together, the teams have collected data across real-world environments, including stadiums, urban areas, and office and university campuses, to train and validate the system under diverse operating conditions.
“We want to collect as much environmental data as we can to further enhance our model training because our model needs to see different scenarios like a rainy day, a sunny day, and also during the event, before the event and after the event,” Wang said. “We want our model to be aware of those environments and get sufficiently trained.”