UTA Engineer Develops Remote Radar to Detect Drone Failures, No Sensors Required

As drones become increasingly common in delivery networks and surveillance operations, early detection of mechanical issues is critical.

A University of Texas at Arlington engineer has developed a radar-based system that can remotely diagnose drone malfunctions, without needing to install sensors or onboard equipment.

The system, created by Dianqi Han, assistant professor in UTA’s Computer Science and Engineering Department, uses millimeter-wave radar to detect subtle signs of trouble, such as irregular vibrations or damaged propellers, from a distance.

Dianqi Han, assistant professor, Computer Science and Engineering Department, College of Engineering [Photo: UTA]

“We’re using high-resolution millimeter-wave radar to spot tiny mechanical irregularities in drones before they lead to serious failures,” Han said. “Our goal is a lightweight, cost-effective solution that keeps drone fleets operating safely and efficiently—even at scale.”

A ground-based approach to drone safety

The project, titled Remote Diagnosis of Drone Hardware Malfunctions Using mmWave Sensing, is supported by UTA’s Research Enhancement Program (REP), which provides seed funding to help faculty pursue new research directions and develop proposals for external funding.

UTA said Han’s system has been tested successfully in lab environments. Results show that millimeter-wave radar can detect early warning signs of hardware problems, offering a proactive, remote approach to monitoring drone health.

“As drones play a larger role in logistics and commercial operations, aging hardware and wear-and-tear present safety risks,” UTA said in its announcement. “Issues like failing sensors or propeller damage can lead to crashes if not addressed early.”

Next steps: Real-world readiness

Han’s team is working to make the system more robust for unpredictable outdoor conditions. Because it’s ground-based and uses commercially available radar with minimal computing power, UTA says the technology could scale across drone delivery, performance monitoring, and surveillance applications.

By detecting problems without modifying the drone itself, the system could help prevent accidents, reduce downtime, and boost safety as autonomous flight becomes more widespread.

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