## 1 Introduction  
Remote sensing technologies have long been used for mapping the static part of natural and man-made environments, including the shape of the Earth, transportation infrastructure, and buildings. As sensing technologies continue to advance, more powerful remote sensing systems have been developed [1], allowing us to observe and monitor the dynamic components of the environment. Light detection and ranging (LiDAR), as remote sensing technology, is already successfully applied to track pedestrian movements [2; 3; 4] and ground vehicle traffic from stationary [5] or on-board sensors [6; 7]. Similar to ground vehicle or pedestrian traffic monitoring, airfield operations, such as taxiing, takeoffs and landings, can be observed and tracked using LiDAR sensors installed on the airfield. Safety at airports is a serious concern worldwide, since 30% of fatal accidents occurred during landing and takeoff and 10% during taxiing or other ground operations between 2007 and 2016 based on the Boeing Company's annual report [8]. Aircrafts motion around airports are tracked by RADAR,but airfield surveillance primarily relies on human observations from control towers. Clearly, active remote sensing in general is able to reduce the human error factor and operates in 24/7 in bad visibility conditions [9]. This feasibility study examines the achievable accuracy of a LiDAR-based monitoring system installed at an airfield to track and derive the trajectory of taxiing aircraft. At close range, LiDAR allows for directly capturing a more detailed and accurate 3D representation of taxiing or landing aircraft compared to existing radar or camera systems due to the higher spatial resolution. The 3D data enables accurately estimating driving patterns or trajectories, detecting aircraft types, monitoring aircraft operations in general, and can be applied to pilot education and training. Analysis of a large number of trajectories can be used for creating stochastic models of the airplane behavior during various operations, and this information can then be used for airport infrastructure and operation planning. Various researchers have already investigated the LiDAR technology for improving airport safety [9; 10], and our earlier studies [11; 12] were the first attempt to use LiDAR sensors to track aircraft ground movements. As the continuation of our previous work, in this study, we apply a more complex sensor network, rigorously address the sensor georeferencing, and for the first time, track airplanes during landings. The main contribution of the paper is the introduction of a novel algorithm to derive motion parameters and extract the trajectory of objects from sparse point clouds. Unlike terrestrial laser scanners that are able to produce large and detailed point clouds over a long scan time, mobile LiDAR sensors applied in autonomous vehicle or robotic applications capture sparse point clouds, but with high temporal resolution; typically, at 10-30 Hz. These sensors make complete 360\({}^{\circ}\) scans around the rotation axis and capture data along multiple planes, producing high resolution in these planes. For instance, the Velodyne VLP-16 [13] and HDL-32E [14] LiDAR sensors, used in this study, have a 30\({}^{\circ}\) and 41.3\({}^{\circ}\) field of view, respectively, and the 16 and 32 planes represent an angular resolution of 1.33\({}^{\circ}\) and 2\({}^{\circ}\), which much coarser than the 0.1\({}^{\circ}\)-0.3\({}^{\circ}\) resolution around the rotation axis. These sensor characteristics translate to only about 10-20 points per scan captured from a Cessna-sized airplane at a 40-m object-sensor distance. Note that the minimum distance to deploy sensors around runways and taxiways is regulated by the airport authorities for safety reasons. Clearly, the challenge is to track aircraft from these low-resolution LiDAR scans. The algorithm addressing this issue and presented in this paper could be of interest to autonomous vehicle applications or robotics to track objects that are farther away from the LiDAR sensor.