Being a conscientious certified drone/ RPAS pilot I have spent quite a bit of time thinking about and researching this type of potential of conflict between low flying aircraft and our drones, so I was thrilled to hear DJI announce last year that it was attempting to remediate this situation by using a new capability that they called AirSense which overlays ADS-B “In” information onto our controller’s map view, plus provide notifications when aircraft equipped with ADS-B “Out” have been detected in the area. AirSense is only available in new DJI products over 250 grams, built since January 2020; and unfortunately I've confirmed that our older drones cannot be retrofitted with this capability.
Luckily, as others have already discussed, this ADS-B information can also be consumed from web sites and apps such as FlightRadar, FlightAware, adsbexchange, etc., where you have Internet connectivity, but these sites rely on volunteers collecting and sending the ADS-B information that they receive to remote hosted servers. What you see therefore has inherent dlays and is not real-time. The challenge here is that some geographical areas may not be adequately covered by volunteers or the drone pilot may not have any Internet connectivity to the data for these apps or web sites from their current location.
Also this information is graphical in nature and the drone pilot simply cannot afford to divert their Visual Line Of Sight (VLOS) attention from the RPAS to then focus on and analyse something else; as most country’s federal aviation regulations dictate that the pilot must have constant VLOS at ALL times with their vehicle whilst airborne. This creates a conflict for the pilot and makes the monitoring of displays for ADS-B or any other information difficult for the pilot, who could easily lose sight of their aircraft while processing this information.
Open areas away from airfields are often used by helicopters which can quickly and unexpectedly appear at very low levels and sometime even land nearby, thus having an unplanned and profound impact on the drone’s area of operations. Plus military aircraft, which use remote training areas, are often travelling fast and operating at low-levels and thus appear from out of nowhere without warning; and unfortunately they don’t appear on the above apps (except adsbexchange) as they transmit MLAT instead of ADS-B telemetry.
In the current implementation of AirSense, the pilot’s attention is required to be diverted from their VLOS of their drone to the controller’s screen for the pilot to be able to analyse the situation and determine the next appropriate action. It makes sense to me that this situational awareness information should be provided not from a screen, but verbally so as not to distract from the task of flying.
Most of today’s drones have no automated visibility of other localised airborne traffic therefore the drone pilot’s VLOS is limited to the RPAS’s operational area on their visual horizon, resulting in them not being completely situationally aware of any other approaching traffic, either behind, above, or to the side of them. As such using just VLOS the drone pilot only has a restricted “tunnelled” line-of-sight visibility with their vehicle and can be easily surprised by the above mentioned low flying aircraft approaching from outside of their peripheral vision.
For a relatively low cost it is possible to build your own ADS-B receiver using a Raspberry Pi board, a RTL-SDR USB dongle, and a 1090 MHz specially tuned antenna. Enthusiasts have developed software that take ADS-B and MLAT data received by the RTL-SDR dongle and process that data such that it can be displayed textually and optionally positioned onto a map. This telemetry information can be received from any aircraft within line of sight of the antenna, which is sufficient for situational awareness for our drone flights. So it is now entirely possible to take your own FlightRadar type of environment that processes ADS-B data in real time with you into the field.
I’m still researching and currently dusting off my software coding skills with the intent to develop a simple app that analyses this textual data to identify aircraft that have the potential of intersecting with my drone’s area of operations, and then to use text-to-speech capabilities to announce to me verbally that a situation exists, without me having to look away from my drone.
MLAT information, such as that transmitted from military aircraft for example, doesn’t contain as much information as that broadcast by ADS-B aircraft, so unfortunately it is impossible to determine a potential conflict, but the sheer fact that MLAT information is being received locally will be enough to provide cautionary information to that effect to the drone pilot.
The end goal is to provide a tailored verbalised message such as “traffic entering operational area approaching at x-o-clock*, altitude / ascending from / descending from (altitude height), at a distance of (distance), on a track of (compass bearing) degrees” or something similar. Basically any message can be build (or derived) from information currently seen in the FlightRadar type applications; so call signs, speed, etc., could also be announced if required.
I’ll be happy to explore my thoughts and input from anybody else interested in this.