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OGN network wins EASA GA Safety Award

OGN network was started by Pawel Jalocha in 2012 with the idea of creating an inexpensive ground receiver for FLARM signals, network them and to stream live traffic because doable, fun and useful. The technical approach was brilliant: Instead of creating expensive custom hardware, he took advantage of an inexpensive tunable USB TV receiver (also known as software-defined radio), plugged it into a small Linux computer (also known as Rasberry-Pi and comparable designs), and applied a bit software wizardry to process FLARM signals instead of an episode of “Simpsons”.

Every OGN station receives signals from FLARM and later also other conspicuity (or how EASA has recently started to name them: iconspicuity) devices. The traffic data is streamed via the Internet to a cloud server, then distributed to a growing number of applications, both on the ground and in the air. These include visualization, flight tracking, flight time logging, search-and-rescue, archiving, accident investigation assistance, airspace design assistance, re-broadcasting for non-interoperable other conspicuity devices and more. Thus the purpose has clearly gone into the safety domain.

OGN quickly grew thanks to its active community, offering attractive additional services for aviation, the basic ones mostly for free, some at cost (especially when related to hardware), some in a commercial context. Today, its nearly 2’000 receivers cover large parts of Europe and many smaller areas in the rest of the world. The majority of data processes and airspace users tracked with OGN even today is FLARM, used by more than 50% of all aircraft and an exploding number of other users in Europe and the rest of the world.

Today, we are pleased to learn that OGN and its current main coordinator, Sebastien Chaumontet, were awarded the EASA GA Safety Award. We fully support this decision and congratulate Seb, Pawel, Philippe Boissel, Wojtek Buczak, Melissa Jenkins Andersson, Gerhard Wesp, Angel Casado (the most active contributors in the past and present) and the whole community of broadcasting airspace users and receiver operators on this achievement.

We did not initially buy into the idea of OGN when Pawel first expressed his early ideas when working for us in 2011, and we voiced strong concerns on several legal and IP aspects around OGN in 2014/2015. In parallel, we also actively contacted the team behind OGN starting in 2014 and offered assistance. Starting in 2015, we improved the radio communication in order to make OGN better, added several features to accommodate the extended needs from pilots being exposed to public tracking via OGN, and ensured OGN’s decoding staying interoperable through all major updates released so far.

Looking forward we wonder: If EASA endorses non-certified, direct-broadcast and non-networked electronic conspicuity technology in manned aviation – why is the same not possible for UAV? Why do we instead mandate complex, distributed, expensive, and fragile systems that can only ever be built multi-billion-dollar companies?


Fire and Rescue Services equip UAVs with FLARM

An increasing number of Fire and Rescue departments are strengthening their equipment fleets with FLARM-equipped UAVs. The newest addition to the list is “Schutz und Rettung Zürich” (“Fire and Rescue Zurich”). They now operate two DJI Matrice 210 with a FLARM traffic information and collision avoidance system from the company Remote Vision. This makes it possible for them to fly close to airports and in restricted areas where drone traffic is otherwise not allowed.

The UAVs are equipped with thermal imaging cameras to be able to assess fire temperatures, find missing persons, as well as other use cases.

The Swiss magazine “Neue Zürcher Zeitung” has published a story about the drones (in German).

New 2020 Obstacle Databases Released

Today, we released the annual update of the obstacle databases. As communicated last year, obstacle databases are now available for several new areas. In addition to the Alps database, the following areas are now available:

  • Austria & Slovenia
  • France
  • Germany
  • Northwest Italy
  • Northeast Italy
  • Switzerland
  • UK & Ireland

The new obstacle databases come with a 1-year license and include many new obstacles. Make sure to update before your next flight.

NASA starts using FLARM for drone UTM

NASA’s Langley Research Center has started using FLARM in its Pathfinder drone UTM project. The goal of Pathfinder is to take separate Urban Traffic Management (UTM) projects and combine them into a single autonomous vehicle, then have that vehicle fly and communicate with other autonomous vehicles in the airspace.

“Pathfinder was conceived as a way to perform a graduation exercise for a lot of the UTM projects we developed over the years,” said Lou Glaab, assistant branch head for the Aeronautic Systems Engineering Branch in Langley’s Engineering Directorate and Pathfinder project manager.

Part of that graduation exercise is the Independent Configurable Architecture for Reliable Operations of Unmanned Systems (ICAROUS).

“We’re testing things like ICAROUS, which is an autonomous sense and avoid flight management system for unmanned systems, as well as Safe-2-Ditch, which is an autonomous safe landing or autonomous crash management system,” said Glaab.

FLARM is being used as part of ICAROUS both to avoid manned aircraft as well as other drones.

“To operate autonomously you need several capabilities, especially in the UAS domain,” said Swee Balachandran, research engineer. “You need to be able to make decisions to avoid other intruders in the air space, stay clear from no-fly zones, or inside a no-fly zone the UAV should know how to get out of it and to re-route itself around obstacles and no-fly areas.”

Balachandran also said that these functionalities are essential to operate autonomously without human intervention. That’s where ICAROUS comes in.

One of the key things in the development of ICAROUS was formal verification.

“Every algorithm that you develop goes through a rigorous mathematical process and we have certain properties, and we ensure the algorithms satisfy those properties so that it is safety-critical and you don’t see unwanted behaviors in flight,” said Balachandran.

In the video below, NASA explains the goals of the Pathfinder project.

Introducing Continuous Range Analyzer

How do you know if the radio range of your FLARM installation is sufficient?

As you should know, verifying the radio range is crucial for any FLARM installation: cables, connectors, and antennas may slowly deteriorate due to oxidation, mechanical stress, or simply aging. Hence, the radio range should be checked regularly by using the online Range Analyzer tool.

The Range Analyzer works with one or more FLARM flight logs (IGC files). It uses data from received traffic for a statistical analysis, which is then presented graphically. For meaningful results, it requires a large number of contacts during flight – a record from a nice summer day is better than one from a night flight in the winter.

Storing all proximate traffic in the memory of the device is not possible. Until now, a majority of the data had to be discarded and could not be used by the range analyzer. The just-released CARP, or Continuous Analyzer of Radio Performance, solves this with a clever trick: The range statistics operations are performed on-the-fly, while the data is being received. The statistical range is continuously integrated over time with each aircraft that comes within, or disappears out of, range. Thanks to CARP, less flight time will be needed to get a range measurement and the measurement will be more reliable.

An example of the new CARP Range Analyzer results is shown below.

With FLARM firmware version 6.80 and later, CARP range data is automatically written to IGC files being recorded on the FLARM device after each flight. Each IGC file will thus contain both the new CARP data and the classic non-integrated, but quantitatively limited, data; the latter being used for e.g. SAR purposes.

Since CARP integrates the data over time without any restriction, it has to be reset manually when needed. If the data collection period is too long, a recent degradation in the installation quality might not be visible. It’s advisable to reset CARP at least once per year, e.g. during annual maintenance, after the old CARP data (latest IGC file) has been read out and retained. CARP is normally reset using the connected FLARM Compatible display. For displays that don’t have this capability, CARP can also be reset using the Configuration Tool. To avoid reconfiguring the device, a config file that only resets CARP can also be downloaded here.

CARP is available in all PowerFLARM-based devices which have firmware version 6.80 or above.

TrafficView from LXNAV certified

The TrafficView and TrafficView57 displays from the company LXNAV have now been certified as FLARM Compatible in the category ‘Primary Display’. Certification in this category makes it possible to install the display as the main display connected to FLARM devices that don’t have their own display.

Features like the new Alert View, Head-On Alert view, and a TCAS view will help pilots quickly identify threats and to decide on the appropriate resolutive action.

The displays can be found, together with other certified displays, under the ‘Primary Displays’ category in the Product Selector.

German UTM system based on FLARM

Droniq, a joint venture between the German ANSP DFS and Deutsche Telekom, is offering the first operational drone traffic management system in Europe and paves the way for safe and efficient drone operations in shared airspace and BVLOS.

Drones are detected with the help of a drone traffic management system (UAS traffic management system, UTM, U-Space) based on DFS’ air traffic control system and the Deutsche Telekom mobile communications network. For the drone to be detected, the company has developed a special LTE modem with an integrated SIM card, the so-called hook-on device (HOD). After this device is hooked on the drone, it transmits the position of the drone and its identification to the UTM system using the mobile communications network. In addition to basic data, the network can also be used to transfer steering commands and other information.

In the airspace close to the ground, there is currently no radar coverage and airspace users generally fly according to visual flight rules. For this reason, aircraft or helicopters generally use the traffic awareness and collision warning system FLARM which continuously transmits position and other flight data. The devices receive FLARM and forward the data via mobile communications network the UTM cloud. In this way, the drone pilot always receives a precise overview of all flight movements in the vicinity. In addition, the HOD also transmits its own position data via FLARM, so a manned aircraft near a drone with a HOD will automatically receive a warning in the cockpit although he doesn’t even use the UTM.

The first released version of the UTM is available and already being used operationally by companies in the chemical and energy industry. Droniq service covers the entire spectrum for drone flights beyond the visual line of sight.

New FLARM displays from AIR Avionics certified

Two new models of the AIR Traffic Display series from the company AIR Avionics have today been certified as FLARM Compatible in the category ‘Primary Display’. Certification in this category makes it possible to install the display as the main display connected to FLARM devices that don’t have their own display.

The new displays ATD-11 and ATD-80 complement the previously certified display ATD-57, which is to date one of the most commonly installed FLARM displays, especially in powered aircraft. ATD-80 is a larger version of the ATD-57 and fits in a standard 80 mm (3 1/8″) cutout. The ATD-11 is a rectangular model with a large screen, intended for external mounting.

Many functions of the AIR Traffic Displays have been developed as a result of research projects at leading research facilities, such as the Institute of Flight Systems and Automatic Control at the Technical University of Darmstadt.

The displays can be found, together with other certified displays, under the ‘Primary Displays’ category in the Product Selector.

Computer Vision to Assist Pilots

See-and-avoid is still the gold standard for collision avoidance in aviation, especially in VFR. There is simply no system that can detect all traffic, from airliners to single-engine piston aircraft, paragliders, and UAVs. Looking out is still part of the routine of even airliner pilots – especially while flying through Class E airspace shared with General Aviation (as is shockingly common, for instance, in Germany and Switzerland).

Alas, human vision is notoriously limited and pilots have more than just this one job to focus on. So why not help the humans (and similarly, air traffic control on the ground) by pointing out traffic they may have missed, complementing cooperative systems like transponders/TCAS, ADS-B, and FLARM?

Together with the Computer Vision Laboratory (CVLAB) of the EPFL in Lausanne, we have developed a leading-edge architecture together with a set of algorithms to do exactly that. It detects and classifies other manned aircraft and some other hazards in a live video stream from one onboard camera based on hardware that can fit into a GA cockpit. This information can then be used to track and locate targets, to issue warnings to the pilots and even to automatically recommend and execute avoidance maneuvers. Some algorithms are based on the well-researched YOLO architecture, using tools from deep learning and convolutional neural networks (CNN) to achieve high accuracy while running in real-time. Other algorithms apply common computer vision algorithms.

A crucial part of deep learning methods is to have a large set of sample data that can be fed to the algorithm for training. The data first needs to be annotated by humans, which can be a very tedious task. CVLAB has developed tools to make this efficient and together we have annotated a huge set of video data from many flights conducted under various traffic and environmental conditions.

The first video below is made by EPFL to explain the project, the challenges that they faced and how they were solved. The second video shows a clip from the onboard camera with aircraft identification annotations made by the deep learning algorithms.