Radar is an important sensing technology for three-dimensional positioning of aircraft. This method requires detecting the response from the object to the signal transmitted from the antenna, but the accuracy becomes unstable due to effects such as obstruction and reflection from surrounding buildings at low altitudes near the antenna. Accordingly, there is a need for a ground-based positioning method with high accuracy. Among the positioning methods using cameras that have been proposed for this purpose, we have developed a multisite synchronized positioning system using IoT devices equipped with a fish-eye camera, and have been investigating its performance. This report describes the details and calibration experiments for this technology. Also, a case study was performed in which flight paths measured by existing GPS positioning were compared with results from the proposed method. Although the results obtained by each of the methods showed individual characteristics, the three-dimensional coordinates were a good match, showing the effectiveness of the positioning technology proposed in this study.

At civil airports and military facilities, various aircraft types are operated. For the mitigation and management of aircraft noise, several surveys and studies have been conducted around airports. Generally, to build a valuable database from these aircraft noise observation data, it is necessary to identify the aircraft types. To achieve this, we are developing an AI model that identifies aircraft types by applying machine learning techniques and using measured acoustic aircraft noise data. In this study, to improve the generalization performance of the model by expanding the dataset for training used in machine learning, we applied Swarm Learning technology, which enables machine learning to be executed under distributed conditions without centralizing measured noise data, and verified its accuracy. In the study, the accuracy of convolutional neural networks using general procedures with all datasets was compared with the results analyzed using Swarm Learning, where the dataset was divided into several groups. As a result, although Swarm Learning showed a slight decrease in accuracy compared with convolutional neural networks, its accuracy remained very high at 94%, demonstrating that it is a sufficiently effective method considering the effort required to centralize measured data in one location.