Identifying Aircraft Motions and Patterns from Magnetometry Data Using a Knowledge-Based Multi-Fusion Approach.

In aviation there are many safety-critical domains where reliable safety systems are essential to prevent any kind of hazard. This paper focuses on airport aprons, where currently used holding point protection systems have shown to be not faultless, sometimes leading to avoidable accidents. One way to avoid such accidents is by means of innovative sensor technology, in our case, magnetometers, i.e. sensors measuring the distortion of the earth’s magnetic field by metallic objects. The main goal is to use the magnetometry data to detect passing aircraft and to capture their geometrical pattern as well as to estimate their motion vector. Therefore, we present a spatio-temporal cluster fusion and an event fusion algorithm. The cluster fusion can be applied as a post-processing step to any spatio-temporal clustering method and is able to more accurately represent aircraft patterns by integrating expert knowledge into the fusion process. In this context, we present a spatio-temporal cluster tree representation for a fast and accurate estimation of the motion vector. Finally, the data-driven event fusion is able to separate detected aircraft crossings into separate events by employing domain-knowledge. In future work, we aim to come up with a framework making use of the cluster results and estimated motion vector to classify and infer the position of an aircraft, before this is deployed as a real-time application.