At Fuse topping out ceremony, student researchers predict malicious intent from drone flight patterns


With today’s use and demand for all-access video, small Uncrewed Aerial Vehicles (UAVs), colloquially known as drones, are commonplace tools in everything from defense contracting to filming backyard barbeques.

While useful technology, UAVs can pose a serious security risk. George Mason University systems engineering and operations research undergraduates Dyar Aziz and Markus Garretson are working with professor Ali Raz to develop a methodology for inferring UAV intent based on sensor data. 

Tell us about your project. 

UAVs are becoming ubiquitous for commercial, defense, and hobbyist operations, but pose a serious security risk to various defense and state installations (such as airports and military bases). While sensors can provide detection and tracking information for UAVs, it is vital to infer any potential malicious intent of the UAV so that counter measures can be put in place in a timely manner.  

Our project focuses on predicting the state and intent of UAVs from uncertain sensing data. This work shows how an inference engine for UAV state-estimation and intent can be built by exploiting sensing and tracking data from a multisensor installation, using state-of-the-art, high-level information fusion and systems engineering tools. We are applying model-based systems engineering techniques to simulate multisensor fusion and determine a UAV’s (specifically drones) movement and behavior. 

What inspired you to select this topic? 

We were introduced to the project by our former professor Raz and decided to work on it as we are both interested in aerospace. 

What has been your greatest success thus far? 

We have been able to develop multiples models and scenarios simulating both drones and real-world use cases of drone tracking such as a radar installment at an airport. 

What is one thing you’ve learned while working on this project that will be useful to you in the field? 

We have developed our knowledge and ability to apply model-based systems engineering (SysML), as well as building simulations in MATLAB. Additionally, we have learned about data fusion, fusion techniques, sensor types, and potential uses for them. Finally, we have been able to gain insight into the future of counter drone system development and high-level data fusion as it is at the cutting edge of technology. 

How would dedicated research space, such as the plans for Fuse at Mason Square, help sustain your project? 

A dedicated research space would enable us to collaborate amongst our team and with other teams with similar experience and knowledge, as well as with teams from other disciplines.