## 5 Discussion
### Limitations and Possible Applications  
The prototype system allows for real-time tracking of aircraft during taxiing, landing or takeoffs. Figure 17 shows an example of a user interface, where a landing aircraft is tracked, and the demonstration system provides the velocity, altitude and position including the error envelopes along with video feedback. Currently, the main limitation of the developed system is the small observation space, which is an affordability issue. To cover larger sections along runways and taxiways would require large number of sensors, which would increase the price of the system and the operation costs. However, mobile LiDAR scanners, such as Velodyne sensors, are expected to become cheaper in the coming years due to the increasing needs for mass production boosted by the autonomous car industry. There are other applications where the proposed system is applicable at airports. For instance, LiDAR might offer tracking solution in night or bad visibility conditions, when optical sensing is compromised. Obviously, LiDAR will fail in extreme weather conditions, such as heavy raining or snowing. With improving resolution and coverage, aircraft types can be the detected and models identified. Figure 18 shows the reconstruction of a Cessna and a Learjet, where details seem to adequate for aircraft model identification. Note that the scale is preserved in LiDAR data, making object identification easier. Clearly, machine learning or other algorithms can be developed to identify the types of the incoming airplanes captured by the LiDAR sensor network [9]. In addition, LiDAR as remote sensing technology, can also be used for monitoring the surroundings to detect and identify hazardous objects to improve the safety of apron operations (see [9; 10]).  
Figure 17: Aircraft tracking: (**a**) current video frame, (**b**) altitude profile with blue dashed line showing the error envelope in cm, red line is the runway surface, and (**c**) 2D trajectory.  
Besides the possible applications presented above, the original intent of this effort was to develop a LiDAR-based prototype system to demonstrate the feasibility that airplanes can be identified and tracked in airport environment without any cooperation from the aircraft. Note that aircraft navigation systems provide this information to the crew, but this data is not shared by airport authorities. Using remote technology provides non-cooperative alternative for collecting huge amount of motion parameters that can improve airport safety through helping understand and assess the risk of the pilot's driving patterns on the runways and taxiways (e.g., centerline deviation, wingspan separation). This information can be used in aircraft and airport planning, e.g., whether the airport meets the standardized criteria or providing data for modification of airport standards and pilot education. To develop adequate risk models for taxiing behavior, [PERSON] et al. used positioning gauges to obtain data at the Chiang-Kai-Shek International Airport [24]. Another study from FAA/Boeing investigated the 747s\({}^{\prime}\) centerline deviations at JFK and ANC airports [25; 26; 27]. They used laser diodes at two locations to measure the location of the nose and main gears. These approaches allow only for accurate on-ground aircraft tracking as opposed to the LiDAR-based tracking system that is also capable of tracking during landing or takeoffs with high accuracy. Figure 19 shows the trajectories of all landing airplanes during Test #2.