Our paper "Classifying Aircraft Categories from Magnetometry Data Using a Hypothesis-based Multi-Task Framework", which is a joint work of Julian Vexler and Stefan Kramer, has been accepted at the 12th International Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 (https://ecai2023.eu/ECAI2023-3), as a sister conference to ECAI 2023, the 26th European Conference on Artificial Intelligence. The paper is a result of an on-going project about the air-side integration of magnetometers for object classification.
Airport traffic surveillance requires reliable safety systems to prevent accidents in safety-critical areas. This paper examines airport aprons, where existing holding point protection systems have shown that they are sometimes not able to prevent accidents. One possible solution to this problem is the use of innovative sensor technology such as magnetometers. These sensors can be used to measure the distortion of the earth's magnetic field by metallic objects. The main objective is to identify the geometrical pattern of a passing object by fusing coherent events, and classify it into a category based on its size. We propose a hypotheses-based multi-task framework for the classification of aircraft by making use of the estimated motion behaviour of a passing object. The framework includes statistical components, domain knowledge, and artificial intelligence solutions to infer the geometrical pattern and motion vector of an object from a predefined set of possible hypotheses. In future work, we aim to optimize the framework using synthetic and real-world data to increase its robustness and generalization ability to other airports.