Set-based Multi-Sensor Data Fusion For Integrated Navigation Systems

This paper presents a novel set-based multisensor data fusion algorithm for combining aircraft 3D position estimates provided by three separate positioning systems: Inertial Reference System (IRS), Global Positioning System (GPS) and Instrument Landing System (ILS). An Extended Zonotopic Kalman Filter (EZKF) is proposed to solve the problem of IRS/GPS/ILS data fusion that rigorously encloses the nonlinearities of ILS measurement equations. Moreover, an adaptive tuning of the overall data fusion filter relies on a lower layer integrating a bank of elementary filters. The latters result from the simplification of firstorder zonotopic Kalman filters optimizing a 1-norm accuracy criterion. Simulations using real flight data provided by Airbus illustrate the effectiveness of the proposed method.