Unmanned Aerial Vehicles aren’t necessarily the first thing one associated with Ford Motor Company, but the US automaker is, nonetheless, rather interested in the rapid proliferation of drone technology.The company’s Palo Alto-based UAV Systems group has even developed a customizable drone development platform that allows the automaker to study and test for integration between unmanned aircraft and its range of cars, trucks, and SUVs.
Now, Ford Motor Company is presenting a rather simple possible solution to the problem of how to identify and track UAVs: using drones’ onboard collision lights to beam their 10-digit FAA registration numbers for capture and decoding.
This unique proposal would easily identify drones in close range with little to no modification of existing models. This work will go on to ensure the safe and responsible use of drones in U.S. airspace while maintaining the bandwidth necessary for innovation, as the recommendations given to the FAA could help lay the foundation for drone flights over people and beyond visual line of sight.
Specifically, this solution leverages the 10-digit code the FAA provides to those who register their drone, which must be legibly printed somewhere on the frame of the device. The challenge is that a drone’s ID cannot be read unless somebody is in close proximity to the device, making it almost impossible to identify drones that are in flight.
Therefore, Ford looked at using the anti-collision lights that several drones offer to improve their visibility during nighttime operations. Their patent-pending idea is to use the lights to broadcast a drone’s 10-digit code in an ASCII-encoded binary signal at a baud rate — one that could be synced for consistency across the system to ensure universal compatibility.
In addition to these lights being used to visually communicate system status with a combination of colour and blinks, they could send a drone’s FAA registration number so that it can be captured and interpreted by a camera-based software app that the company has developed. Its decoding algorithms, built using Google TensorFlow, can be run on a standard smartphone, which would enable the public to identify and report any misbehaving drones. Already, preliminary in-field tests show the system can consistently and accurately identify drones operating within 80 feet of an observer. Think of the 10-digit code as a license plate number, with this solution allowing people to identify or report a drone that’s operating in a situation where it shouldn’t be.
By using an existing on-board subsystem, it can help operators avoid spending time and money to install additional hardware components on their drone, such as dedicated radio or ADS-B transmitters, making this solution optimal for widespread adoption.
A more detailed look at our submission to the FAA can be found in our white paper titled, “A Zero-Cost Solution for Remote Identification and Tracking of sUAS in Low Altitude Flights.”
Source: Ford