{"id":945,"date":"2019-03-04T13:50:11","date_gmt":"2019-03-04T12:50:11","guid":{"rendered":"http:\/\/www.i3a.uclm.es\/raap\/?page_id=945"},"modified":"2021-10-27T08:09:13","modified_gmt":"2021-10-27T07:09:13","slug":"improvements-in-aircraft-final-approach-procedures","status":"publish","type":"page","link":"https:\/\/www.i3a.uclm.es\/raap\/?page_id=945","title":{"rendered":"Improvements in Aircraft Final Approach Procedures"},"content":{"rendered":"
The main aim of this research is to develop different avionics navigation systems that will allow the aircrafts performing approach and landing maneuvers to do it in a more efficient way. These systems will be supported by state-of-the-art navigation technologies, such as avian radars, GBAS, and ADS-B, and by the new context provided by Performance-Based Navigation (PBN). Our developments can be employed to avoid bird strikes, to efficiently handle aircraft traffic flows or to improve the usual missed approach procedure.<\/span><\/div>\r\n \r\n

\"\"<\/span><\/p>\r\nTo achieve this last goal, we propose adaptive approximation mechanisms based on dynamic paths, which will be applied to the case of a new type of missed approach with aircraft reinjection in the landing flow. This new type of missed approach will also be adaptable to the Point Merge System, a method for sequencing arrival flows that supports continuous descent operations.\r\n

\"\"<\/p>\r\n\"\"\r\n\r\nAs an alternative implementation for these systems, we considered employing deep learning techniques, based on neural networks. Once trained, the neural network should be able to provide the aircrafts\u00a0with the paths to follow, replacing in this way the ATC (air traffic controller) function.\r\n\r\nWith respect our framework, we employ the Matlab\/Simulink software for modeling and simulating the airspace and the final approach procedure. This work environment allows us to design and to test our developments.\r\n\r\nTopics:<\/span>\r\n