Combined Multilateration with Machine Learning for Enhanced Aircraft Localization

Abstract

In this paper, we present an aircraft localization solution developed in the context of the Aircraft Localization Competition and applied to the OpenSky Network real-world ADS-B data. The developed solution is based on a combination of machine learning and multilateration using data provided by time synchronized ground receivers. A gradient boosting regression technique is used to obtain an estimate of the geometric altitude of the aircraft, as well as a first guess of the 2D aircraft position. Then, a triplet-wise and an all-in-view multilateration technique are implemented to obtain an accurate estimate of the aircraft latitude and longitude. A sensitivity analysis of the accuracy as a function of the number of receivers is conducted and used to optimize the proposed solution. The obtained predictions have an accuracy below 25 m for the 2D root mean squared error and below 35 m for the geometric altitude.

In Pursuit of Aviation Cybersecurity: Experiences and Lessons From a Competitive Approach

“…sourcing of the code from the very beginning. Our experiences with the extension of round 2 illustrate the power of this open and collaborative approach; all top results improved significantly, often by learning from the published approaches (code, documentation, and papers, such as Figuet et al 14 ) of round 1. In the meantime, new projects have popped up on Github (for example, https://github.com/radoslawkrolikowski/ adsb-flight-localization), illustrating their own methods and building on the code and data published after the conclusion of round 2.…”